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AbstractCommon variable immunodeficiency (CVID) is a heterogeneous primary immunodeficiency (PID) characterized by an impaired immunoglobulin production, in association with an increased susceptibility to infections and a diversity of clinical manifestations. This narrative review summarizes immunophenotypic abnormalities in CVID patients and their relevance for diagnosis and disease classification. A comprehensive search across four databases - PubMED, Web of Science, EMBASE and Google Scholar – yielded 170 relevant studies published between 1988 and April 31, 2023. Over the past decades, the role of immunophenotyping in CVID diagnosis has become evident by identifying “hallmark” immunophenotypic aberrancies in patient subsets, with some now integrated in the consensus diagnostic criteria. Furthermore, the role of immunophenotyping in subclassifying CVID in relation to clinical presentation and prognosis has been extensively studied. Certain immunophenotypic patterns consistently correlate with clinical manifestations and/or subsets of CVID, particularly those associated with noninfectious complications (i.e. low switched memory B cells, shifts in follicular helper T cell subsets, low naïve CD4+ T cells, low regulatory T cells, and expansion of CD21low B cells, often associated with autoimmunity and/or splenomegaly). Also, efforts to associate subset levels of innate immune cells, such as Natural Killer (NK) cells, invariant (i)NKT cells, innate lymphoid cells (ILCs), and dendritic cells (DCs) to CVID complications are evident albeit in a lesser degree. However, inconsistencies regarding the role of flow cytometry in classification and prognosis persist, reflecting the disease complexity, but probably also cohort variations and methodological differences between published studies. This underscores the need for collaborative efforts to integrate emerging concepts, such as standardized flow cytometry and computational tools, for a more precise CVID classification approach. Additionally, recent studies suggest a potential value of (epi)genetic-based molecular assays to this effort. [ABSTRACT FROM AUTHOR]

Purpose Biallelic pathogenic leptin gene variants cause severe early-onset obesity usually associated with low or undetectable circulating leptin levels. Recently, variants have been described resulting in secreted mutant forms of the hormone leptin with either biologically inactive or antagonistic properties. Methods We conducted a systematic literature research supplemented by unpublished data from patients at our center as well as new in vitro analyses to provide a systematic classification of congenital leptin deficiency based on the molecular and functional characteristics of the underlying leptin variants and investigated the correlation of disease subtype with severity of the clinical phenotype. Results A total of 28 distinct homozygous leptin variants were identified in 148 patients. The identified variants can be divided into 3 different subtypes of congenital leptin deficiency: classical hormone deficiency (21 variants in 128 patients), biologically inactive hormone (3 variants in 12 patients), and antagonistic hormone (3 variants in 7 patients). Only 1 variant (n = 1 patient) remained unclassified. Patients with biological inactive leptin have a higher percentage of 95th body mass index percentile compared to patients with classical hormone deficiency. While patients with both classical hormone deficiency and biological inactive hormone can be treated with the same starting dose of metreleptin, patients with antagonistic hormone need a variant-tailored treatment approach to overcome the antagonistic properties of the variant leptin. Main Conclusion Categorization of leptin variants based on molecular and functional characteristics helps to determine the most adequate approach to treatment of patients with congenital leptin deficiency. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has underscored the critical need for precise diagnostic methods to distinguish between similar respiratory infections, such as COVID-19 and Mycoplasma pneumoniae (MP). Identifying key biomarkers and utilizing machine learning techniques, such as random forest analysis, can significantly improve diagnostic accuracy. We conducted a retrospective analysis of clinical and laboratory data from 214 patients with acute respiratory infections, collected between October 2022 and October 2023 at the Second Hospital of Nanping. The study population was categorized into three groups: COVID-19 positive (n = 52), MP positive (n = 140), and co-infected (n = 22). Key biomarkers, including C-reactive protein (CRP), procalcitonin (PCT), interleukin- 6 (IL-6), and white blood cell (WBC) counts, were evaluated. Correlation analyses were conducted to assess relationships between biomarkers within each group. The random forest analysis was applied to evaluate the discriminative power of these biomarkers. The random forest model demonstrated high classification performance, with area under the ROC curve (AUC) scores of 0.86 (95% CI: 0.70–0.97) for COVID-19, 0.79 (95% CI: 0.64–0.92) for MP, 0.69 (95% CI: 0.50–0.87) for co-infections, and 0.90 (95% CI: 0.83–0.95) for the micro-average ROC. Additionally, the precision-recall curve for the random forest classifier showed a micro-average AUC of 0.80 (95% CI: 0.69–0.91). Confusion matrices highlighted the model’s accuracy (0.77) and biomarker relationships. The SHAP feature importance analysis indicated that age (0.27), CRP (0.25), IL6 (0.14), and PCT (0.14) were the most significant predictors. The integration of computational methods, particularly random forest analysis, in evaluating clinical and biomarker data presents a promising approach for enhancing diagnostic processes for infectious diseases. Our findings support the use of specific biomarkers in differentiating between COVID-19 and MP, potentially leading to more targeted and effective diagnostic strategies. This study underscores the potential of machine learning techniques in improving disease classification in the era of precision medicine. [ABSTRACT FROM AUTHOR]

Breast cancer prediction and diagnosis are critical for timely and effective treatment, significantly impacting patient outcomes. Machine learning algorithms have become powerful tools for improving the prediction and diagnosis of breast cancer. The Breast Cancer Prediction and Diagnosis Model (BCPM), which utilises machine learning techniques to improve the precision and efficiency of breast cancer diagnosis and prediction, is presented in this paper. BCPM collects comprehensive and high-quality data from diverse sources, including electronic medical records, clinical trials, and public datasets. Through rigorous pre-processing, the data is cleaned, inconsistencies are addressed, and missing values are handled. Feature scaling techniques are applied to normalize the data, ensuring fair comparison and equal importance among different features. Furthermore, feature-selection algorithms are utilized to identify the most relevant features that contribute to breast cancer projection and diagnosis, optimizing the model's efficiency. The BCPM employs numerous machine learning methods, such as logistic regression, random forests, decision trees, support vector machines, and neural networks, to generate accurate models. Area under the curve (AUC), sensitivity, specificity, and accuracy are only some of the metrics used to assess a model's performance once it has been trained on a subset of data. The BCPM holds promise in improving breast cancer prediction and diagnosis, aiding in personalized treatment planning, and ultimately taming patient results. By leveraging machine learning algorithms, the BCPM contributes to ongoing efforts in combating breast cancer and saving lives. [ABSTRACT FROM AUTHOR]

The rapid and accurate classification of Panax notoginseng leaf diseases is vital for timely disease control and reducing economic losses. Recently, image classification algorithms have shown great promise for plant disease diagnosis, but dataset quantity and quality are crucial. Moreover, classifying P. notoginseng leaf diseases faces severe challenges, including the small features of anthrax and the strong similarity between round spot and melasma diseases. In order to address these problems, we have proposed an ECA-based diffusion model and Inception-SSNet for the classification of the six major P. notoginseng leaf diseases, namely gray mold, powdery mildew, virus infection, anthrax, melasma, and round spot. Specifically, we propose an image generation scheme, in which the lightweight attention mechanism, ECA, is used to capture the dependencies between channels for improving the dataset quantity and quality. To extract disease features more accurately, we developed an Inception-SSNet hybrid model with skip connection, attention feature fusion, and self-calibrated convolutional. These innovative methods enable the model to make better use of local and global information, especially when dealing with diseases with similar features and small targets. The experimental results show that our proposed ECA-based diffusion model FID reaches 42.73, compared with the baseline model, which improved by 74.71%. Further, we tested the classification model using the data set of P. notoginseng leaf disease generation, and the accuracy of 11 mainstream classification models was improved. Our proposed Inception-SSNet classification model achieves an accuracy of 97.04% on the non-generated dataset, which is an improvement of 0.11% compared with the baseline model. On the generated dataset, the accuracy reached 99.44%, which is an improvement of 1.02% compared to the baseline model. This study provides an effective solution for the monitoring of Panax notoginseng diseases. [ABSTRACT FROM AUTHOR]

Chest x‐ray (CXR) is an effective method for diagnosing heart failure, and identifying important features such as cardiomegaly, effusion, and edema on patient chest x‐rays is significant for aiding the treatment of heart failure. However, manually identifying a vast amount of CXR data places a huge burden on physicians. Deep learning's progression has led to the utilization of this technology in numerous research aimed at tackling these particular challenges. However, many of these studies utilize global learning methods, where the contribution of each pixel to the classification is considered equal, or they overly focus on small areas of the lesion while neglecting the global context. In response to these issues, we propose the Global Local Attention Network (GLAN), which incorporates an improved attention module on a branched structure. This enables the network to capture small lesion areas while also considering both local and global features. We evaluated the effectiveness of the proposed model by testing it on multiple public datasets and real‐world datasets. Compared to the state‐of‐the‐art methods, our network structure demonstrated greater accuracy and effectiveness in the identification of three key features: cardiomegaly, effusion, and edema. This provides more targeted support for diagnosing and treating heart failure. [ABSTRACT FROM AUTHOR]

Background: The World Health Organization considers malocclusion one of the most essential oral health problems. This disease influences various aspects of patients’ health and well-being. Therefore, making it easier and more accurate to understand and diagnose patients with skeletal malocclusions is necessary. Objectives: The main aim of this research was the establishment of machine learning models to correctly classify individual Arab patients, being citizens of Israel, as skeletal class II or III. Secondary outcomes of the study included comparing cephalometric parameters between patients with skeletal class II and III and between age and gender-specific subgroups, an analysis of the correlation of various cephalometric variables, and principal component analysis in skeletal class diagnosis. Methods: This quantitative, observational study is based on data from the Orthodontic Center, Jatt, Israel. The experimental data consisted of the coded records of 502 Arab patients diagnosed as Class II or III according to the Calculated_ANB. This parameter was defined as the difference between the measured ANB angle and the individualized ANB of Panagiotidis and Witt. In this observational study, we focused on the primary aim, i.e., the establishment of machine learning models for the correct classification of skeletal class II and III in a group of Arab orthodontic patients. For this purpose, various ML models and input data was tested after identifying the most relevant parameters by conducting a principal component analysis. As secondary outcomes this study compared the cephalometric parameters and analyzed their correlations between skeletal class II and III as well as between gender and age specific subgroups. Results: Comparison of the two groups demonstrated significant differences between skeletal class II and class III patients. This was shown for the parameters NL-NSL angle, PFH/AFH ratio, SNA angle, SNB angle, SN-Ba angle. SN-Pg angle, and ML-NSL angle in skeletal class III patients, and for S-N (mm) in skeletal class II patients. In skeletal class II and skeletal class III patients, the results showed that the Calculated_ANB correlated well with many other cephalometric parameters. With the help of the Principal Component Analysis (PCA), it was possible to explain about 71% of the variation between the first two PCs. Finally, applying the stepwise forward Machine Learning models, it could be demonstrated that the model works only with the parameters Wits appraisal and SNB angle was able to predict the allocation of patients to either skeletal class II or III with an accuracy of 0.95, compared to a value of 0.99 when all parameters were used (“general model”). Conclusion: There is a significant relationship between many cephalometric parameters within the different groups of gender and age. This study highlights the high accuracy and power of Wits appraisal and the SNB angle in evaluating the classification of orthodontic malocclusion. [ABSTRACT FROM AUTHOR]

Agriculture encompasses a way of life and a profession for the general population. Most global traditions and cultures revolve around agriculture. With the help of advanced farming, agriculture may become more profitable, dependable, and able to use resources and time more effectively. The proposed hybrid CGAN using OTSU and SURF (CGAN-OF) model provides a novel framework for plant leaf disease classification. In proposed CGAN-OF model, contrast-limited adaptive histogram equalization is used for image preprocessing and enhancement. The proposed model utilizes OTSU algorithm to speed up the image segmentation without prior knowledge of the images and SURF algorithm to extract the local features using scale-invariant feature transformation. CGAN increases the input plant village dataset using image generation method and identifies the various plant leaf diseases and classifies them. There are three classifications of leaf diseases: fungi, viruses, and bacteria. Furthermore, these classifications contain approximately 300 diseases. The proposed work identified a minimum of 200 diseases in 18,161 major and minor crop species. The investigational investigations are carried out using the Python Jupyter app with the Kaggle Plant Village Dataset and also leaf samples collected from farmers. The proposed framework achieves 99.2% accuracy. [ABSTRACT FROM AUTHOR]

Background: Legumes, such as lentils, field peas, Faba beans and chickpeas, are high in vitamins, fiber, important minerals and protein and can help avoid obesity and cardiovascular illnesses. They also contribute to ecosystem services, such as nitrogen fixation and resilience to environmental stresses. Despite a 60% increase in global pulse production from 2000 to 2021, a demand-supply gap, especially in South Asia, raises concerns about nutritional access. Since illnesses are currently an issue to the food security of faba beans, machine learning is required for efficient disease identification. Methods: This research employs Convolutional Neural Networks (CNNs) for robust Faba bean leaf disease identification. The CNN model is trained with diverse images representing specific diseases. The study focuses on diseases like Chocolate Spot, Faba Bean Gall, Rust and Healthy leaves. Image processing involves resizing, grayscale conversion and labeling. The CNN architecture includes eight convolutional layers, four max-pooling layers and three dropout layers. The model is trained using 80% of the dataset, validated using 20% and tested for accuracy. Result: The CNN model achieves an accuracy of 99.37% during training and 89.69% during validation after 75 epochs. Confusion matrix and classification report illustrate the model’s performance. It shows high precision, recall and F1 scores for each class, indicating balanced performance. Chocolate Spot and Rust exhibit the highest precision and F1 scores. The overall accuracy is 91%, comparable to other studies on Faba bean disease detection. The study presents a CNN-based disease identification system for Faba beans, demonstrating high accuracy and balanced performance across different diseases. The model’s effectiveness is comparable to other advanced techniques. The research highlights the potential of machine learning in optimizing disease management for Faba beans. Future work could explore a broader range of diseases and incorporate hybrid machine learning algorithms for further improvement. [ABSTRACT FROM AUTHOR]

Health datasets often exhibit class imbalance, with healthy individuals being the majority class and patients being the minority class. As in many applications, machine learning methods are frequently used in the prediction and detection of diseases. The class imbalance presents a significant challenge for machine learning classifiers, particularly in their ability to accurately and effectively classify data from minority and majority classes. Therefore, data preprocessing is crucial before classifying the imbalanced data. In this study, we present GASMOTEPSO_ENN method that combines the synthetic minority oversampling technique (SMOTE) and edited nearest neighbour (ENN) algorithms using genetic algorithm (GA) and particle swarm optimization (PSO) heuristics as a preprocessing method to classify the imbalanced health datasets. In the experiments, chronic kidney disease (CKD), cerebral stroke prediction (CSP), and PIMA Indian diabetes (PID) datasets were utilized to assess the performance of the proposed method with metrics derived from the confusion matrix. The GASMOTEPSO_ENN method can classify the various diseases into different two classes of patients and healthy individuals with acceptable Matthews correlation coefficient (MCC) metric using the machine learning algorithms (Logistic regression (LR) 1.00 for CKD dataset, extreme gradient boosting (XGBoost) 0.94 for CSP dataset, and support vector machine (SVM) 0.87 for PID dataset). Moreover, the proposed method also performed well with other metrics in all datasets, and the analysis of the model results in relation to existing literature reveals that the proposed model demonstrably produces superior results. [ABSTRACT FROM AUTHOR]

Background and Aim: Effective clinical event classification is essential for clinical research and quality improvement. The validation of artificial intelligence (AI) models like Generative Pre‐trained Transformer 4 (GPT‐4) for this task and comparison with conventional methods remains unexplored. Methods: We evaluated the performance of the GPT‐4 model for classifying gastrointestinal (GI) bleeding episodes from 200 medical discharge summaries and compared the results with human review and an International Classification of Diseases (ICD) code‐based system. The analysis included accuracy, sensitivity, and specificity evaluation, using ground truth determined by physician reviewers. Results: GPT‐4 exhibited an accuracy of 94.4% in identifying GI bleeding occurrences, outperforming ICD codes (accuracy 63.5%, P < 0.001). GPT‐4's accuracy was either slightly lower or statistically similar to individual human reviewers (Reviewer 1: 98.5%, P < 0.001; Reviewer 2: 90.8%, P = 0.170). For location classification, GPT‐4 achieved accuracies of 81.7% and 83.5% for confirmed and probable GI bleeding locations, respectively, with figures that were either slightly lower or comparable with those of human reviewers. GPT‐4 was highly efficient, analyzing the dataset in 12.7 min at a cost of 21.2 USD, whereas human reviewers required 8–9 h each. Conclusion: Our study indicates GPT‐4 offers a reliable, cost‐efficient, and faster alternative to current clinical event classification methods, outperforming the conventional ICD coding system and performing comparably to individual expert human reviewers. Its implementation could facilitate more accurate and granular clinical research and quality audits. Future research should explore scalability, prompt and model tuning, and ethical implications of high‐performance AI models in clinical data processing. [ABSTRACT FROM AUTHOR]

In modern horticulture, the grape industry across the globe has been coping with the issue of grape crop diseases. The detection of grape leaf diseases using automated methods can greatly assist farmers in mitigating yield losses and ensuring sustainability. However, existing systems face hurdles while handling grape leaf images at the farm level, and these models fail to generalize well on un-seen images. This study proposes the development of a well-curated real-time dataset of grape leaf images assimilated through field visits in the study area in India. This designed dataset is further used to train convolutional neural network models to accurately identify and classify grape leaves as either diseased or healthy. The potential of transfer learning using CNN models like VGG, ResNet, Inception, and Xception is assessed on the curated dataset. Our findings indicate that ResNet50V2 outperformed the other models in accurately identifying and classifying grape leaf diseases. Using transfer learning, existing weights (pretrained) and learned features were utilized for further training and fine-tuning the CNN models on our curated dataset. The results of the proposed approach are compared with existing automated grape leaf disease identification techniques. It is observed that the proposed approach, which is on a real-time grape leaf image dataset, provides the highest accuracy among others. Further, this study provides a wellcurated dataset of on-field grape leaf images in the Indian context, which can serve as a benchmark for future research. This study shows that deep learning techniques can aid farmers in identifying grape leaf diseases early. [ABSTRACT FROM AUTHOR]

Automated recognition of heart shunts using saline contrast transthoracic echocardiography (SC-TTE) has the potential to transform clinical practice, enabling non-experts to assess heart shunt lesions. This study aims to develop a fully automated and scalable analysis pipeline for distinguishing heart shunts, utilizing a deep neural network–based framework. The pipeline consists of three steps: (1) chamber segmentation, (2) ultrasound microbubble localization, and (3) disease classification model establishment. The study's normal control group included 91 patients with intracardiac shunts, 61 patients with extracardiac shunts, and 84 asymptomatic individuals. Participants' SC-TTE images were segmented using the U-Net model to obtain cardiac chambers. The segmentation results were combined with ultrasound microbubble localization to generate multivariate time series data on microbubble counts in each chamber. A classification model was then trained using this data to distinguish between intracardiac and extracardiac shunts. The proposed framework accurately segmented heart chambers (dice coefficient = 0.92 ± 0.1) and localized microbubbles. The disease classification model achieved high accuracy, sensitivity, specificity, F1 score, kappa value, and AUC value for both intracardiac and extracardiac shunts. For intracardiac shunts, accuracy was 0.875 ± 0.008, sensitivity was 0.891 ± 0.002, specificity was 0.865 ± 0.012, F1 score was 0.836 ± 0.011, kappa value was 0.735 ± 0.017, and AUC value was 0.942 ± 0.014. For extracardiac shunts, accuracy was 0.902 ± 0.007, sensitivity was 0.763 ± 0.014, specificity was 0.966 ± 0.008, F1 score was 0.830 ± 0.012, kappa value was 0.762 ± 0.017, and AUC value was 0.916 ± 0.006. The proposed framework utilizing deep neural networks offers a fast, convenient, and accurate method for identifying intracardiac and extracardiac shunts. It aids in shunt recognition and generates valuable quantitative indices, assisting clinicians in diagnosing these conditions. [ABSTRACT FROM AUTHOR]

Fruit cultivation plays a pivotal role in improvement of the agricultural economy. Pomegranate is a nutritionally rich fruit that is highly valuable because of its excellent antioxidant properties, richness in vitamins and fiber. Pomegranate is affected by diseases, which can severely affect its yield and quality. Timely disease detection is vital for effective disease control in pomegranate orchards. This study presents a novel approach for disease detection in pomegranate and categorization of pomegranate using a hybrid Convolutional Neural Network (CNN) coupled with the Honey Badger Optimization Algorithm (HBOA). The proposed hybrid CNN-HBOA approach leverages the strengths of both CNN and metaheuristic optimization algorithm HBOA. The CNN model was designed to extract high-level features from images of pomegranate affected by various diseases, and HBOA was employed to fine-tune the CNN parameters to improve the accuracy and robustness of the model. Initially, the quality of the images was enhanced using a contrast enhancement technique, and the images were segmented using the Detectron2 algorithm. Finally, the extracted features were fed as input to the disease detection stage wherein the CNN classifies the disease. Furthermore, the classification accuracy of the CNN model was enhanced using the HBOA algorithm. The performance of the hybrid CNN-HBOA algorithm was evaluated using a vast collection of images depicting multiple disease categories of pomegranate. Experimental results demonstrate the superiority of the hybrid CNN-HBOA system, which achieves a detection accuracy of 99.58%, precision of 100%, recall of 99.71% and F1 score of 99.75% compared to other existing state-of-the-art models.

Artificial intelligence assists medical practitioners with disease diagnosis from acute to chronic illnesses and has brought significant breakthrough in image-oriented analysis of diseases. Skin cancer is one of the deadliest diseases, posing a great threat to human health. Skin cancer warrants early and precise examination of skin lesions to prevent mortality. Skin lesions can be examined by biopsy, an invasive procedure to remove piece of tissue, tested in a laboratory. Biopsy is a painful procedure. The predictive models in artificial intelligence are found to be useful in analyzing skin lesions-based images. Hence, in this paper, we have analyzed 10,015 skin lesion-based images and classified seven types of skin diseases among which, one of them is melanoma, a form indicative of skin cancer. This work proposes, a novel APFB (Adaptive Percentage Filger for Binarization) to remove the hairline noise from the skin images. The performance of the APFB filter is compared with the state-of-art hairline noise removal methods such as Contrast Limited Adaptive Histogram Equalization (CLAHE) and Bilateral methods and proven to be best in terms of image quality analysis metrics such as PSNR, SSIM, BRISQUE and NIQE. The original images and hairline noise removed images are then passed into custom built CNN models for skin disease classification. The model examines the lesions features like pigment, texture, color, lines, shapes and symmetry to classify the different types of skin diseases. Among the four CNN models we created, the CNN model that takes images of APFB hair line noise removal method obtains 99.98% training accuracy and 100% testing accuracy than the other models. Thus, the model may also be used to predict skin cancer without a biopsy. [ABSTRACT FROM AUTHOR]

Aims: This study aims to develop an advanced model for the classification of Diabetic Macular Edema (DME) using deep learning techniques. Specifically, the objective is to introduce a novel architecture, SSCSAC-Net, that leverages self-supervised learning and category-selective attention mechanisms to improve the precision of DME classification. Methods: The proposed SSCSAC-Net integrates self-supervised learning to effectively utilize unlabeled data for learning robust features related to DME. Additionally, it incorporates a category-specific attention mechanism and a domain-specific layer into the ResNet-152 base architecture. The model is trained using an ensemble of unsupervised and supervised learning techniques. Benchmark datasets are utilized for testing the model's performance, ensuring its robustness and generalizability across different data distributions. Results: Evaluation of the SSCSAC-Net on multiple datasets demonstrates its superior performance compared to existing techniques. The model achieves high accuracy, precision, and recall rates, with an accuracy of 98.7%, precision of 98.6%, and recall of 98.8%. Furthermore, the incorporation of self-supervised learning reduces the dependency on extensive labeled data, making the solution more scalable and cost-effective. Conclusions: The proposed SSCSAC-Net represents a significant advancement in automated DME classification. By effectively using self-supervised learning and attention mechanisms, the model offers improved accuracy in identifying DME-related features within retinal images. Its robustness and generalizability across different datasets highlight its potential for clinical applications, providing a valuable tool for clinicians in diagnosing DME effectively. [ABSTRACT FROM AUTHOR]

Introduction: Multiple sclerosis (MS) and neuromyelitis optic spectrum disorder (NMOSD) are mimic autoimmune diseases of the central nervous system with a very high disability rate. Their clinical symptoms and imaging findings are similar, making it difficult to diagnose and differentiate. Existing research typically employs the T2-weighted fluid-attenuated inversion recovery (T2-FLAIR) MRI imaging technique to focus on a single task in MS and NMOSD lesion segmentation or disease classification, while ignoring the collaboration between the tasks. Methods: To make full use of the correlation between lesion segmentation and disease classification tasks of MS and NMOSD, so as to improve the accuracy and speed of the recognition and diagnosis of MS and NMOSD, a joint model is proposed in this study. The joint model primarily comprises three components: an information-sharing subnetwork, a lesion segmentation subnetwork, and a disease classification subnetwork. Among them, the information-sharing subnetwork adopts a dualbranch structure composed of a convolution module and a Swin Transformer module to extract local and global features, respectively. These features are then input into the lesion segmentation subnetwork and disease classification subnetwork to obtain results for both tasks simultaneously. In addition, to further enhance the mutual guidance between the tasks, this study proposes two information interaction methods: a lesion guidance module and a crosstask loss function. Furthermore, the lesion location maps provide interpretability for the diagnosis process of the deep learning model. Results: The joint model achieved a Dice similarity coefficient (DSC) of 74.87% on the lesion segmentation task and accuracy (ACC) of 92.36% on the disease classification task, demonstrating its superior performance. By setting up ablation experiments, the effectiveness of information sharing and interaction between tasks is verified. Discussion: The results show that the joint model can effectively improve the performance of the two tasks. [ABSTRACT FROM AUTHOR]

Background: Deep‐learning (DL) methods are rapidly changing the way researchers classify neurological disorders. For example, combining functional magnetic resonance imaging (fMRI) and DL has helped researchers identify functional biomarkers of neurological disorders (e.g., brain activation and connectivity) and pilot innovative diagnostic models. However, the knowledge required to perform DL analyses is often domain‐specific and is not widely taught in the brain sciences (e.g., psychology, neuroscience, and cognitive science). Conversely, neurological diagnoses and neuroimaging training (e.g., fMRI) are largely restricted to the brain and medical sciences. In turn, these disciplinary knowledge barriers and distinct specializations can act as hurdles that prevent the combination of fMRI and DL pipelines. The complexity of fMRI and DL methods also hinders their clinical adoption and generalization to real‐world diagnoses. For example, most current models are not designed for clinical settings or use by nonspecialized populations such as students, clinicians, and healthcare workers. Accordingly, there is a growing area of assistive tools (e.g., software and programming packages) that aim to streamline and increase the accessibility of fMRI and DL pipelines for the diagnoses of neurological disorders. Objectives and Methods: In this study, we present an introductory guide to some popular DL and fMRI assistive tools. We also create an example autism spectrum disorder (ASD) classification model using assistive tools (e.g., Optuna, GIFT, and the ABIDE preprocessed repository), fMRI, and a convolutional neural network. Results: In turn, we provide researchers with a guide to assistive tools and give an example of a streamlined fMRI and DL pipeline. Conclusions: We are confident that this study can help more researchers enter the field and create accessible fMRI and deep‐learning diagnostic models for neurological disorders. [ABSTRACT FROM AUTHOR]

One of the most used diagnostic imaging techniques for identifying a variety of lung and bone-related conditions is the chest X-ray. Recent developments in deep learning have demonstrated several successful cases of illness diagnosis from chest X-rays. However, issues of stability and class imbalance still need to be resolved. Hence in this manuscript, multi-class lung disease classification in chest x-ray images using a hybrid manta-ray foraging volcano eruption algorithm boosted multilayer perceptron neural network approach is proposed (MPNN-Hyb-MRF-VEA). Initially, the input chest X-ray images are taken from the Covid-Chest X-ray dataset. Anisotropic diffusion Kuwahara filtering (ADKF) is used to enhance the quality of these images and lower noise. To capture significant discriminative features, the Term frequency-inverse document frequency (TF-IDF) based feature extraction method is utilized in this case. The Multilayer Perceptron Neural Network (MPNN) serves as the classification model for multi-class lung disorders classification as COVID-19, pneumonia, tuberculosis (TB), and normal. A Hybrid Manta-Ray Foraging and Volcano Eruption Algorithm (Hyb-MRF-VEA) is introduced to further optimize and fine-tune the MPNN’s parameters. The Python platform is used to accurately evaluate the proposed methodology. The performance of the proposed method provides 23.21%, 12.09%, and 5.66% higher accuracy compared with existing methods like NFM, SVM, and CNN respectively. [ABSTRACT FROM AUTHOR]

Pine Wilt Disease poses a significant global threat to forests, necessitating swift detection methods. Conventional approaches are resource-intensive but utilizing deep learning on ortho-mapped images obtained from Unmanned Aerial Vehicles offers cost-effective and scalable solutions. This study presents a novel method for Pine Wilt Disease detection and classification using YOLOv8 for segmenting diseased areas, followed by cropping the diseased regions from the original image and applying Deep Metric Learning for classification. We trained a ResNet50 model using semi-hard triplet loss to obtain embeddings, and subsequently trained a Random Forest classifier tasked with identifying tree species and distinguishing false positives. Segmentation was favored over object detection due to its ability to provide pixel-level information, enabling the flexible extension of subsequent bounding boxes. Deep Metric Learning-based classification after segmentation was chosen for its effectiveness in handling visually similar images. The results indicate a mean Intersection over Union of 83.12% for segmentation, with classification accuracies of 98.7% and 90.7% on the validation and test sets, respectively. [ABSTRACT FROM AUTHOR]

Osteoporosis, also known as low bone mineral density (BMD), is a serious health concern, especially for women who have gone through menopause in community settings. This condition weakens the bones and increases the risk of fractures. Women who have gone through menopause are more susceptible to osteoporosis due to hormonal changes. Therefore, it is crucial to identify the condition early to start preventive treatments and reduce the risk of fractures. To address the challenges of diagnosing low BMD in postmenopausal women in community settings, this study proposes a method that combines machine learning with the AdaBoostM1 algorithm, which has shown promising results. Data acquisition, data preprocessing, data training, model testing, and model prediction and evaluation are integral phases of the operational dynamics of our model in osteoporosis diagnosis. This approach recommends increasing screening initiatives and educating patients as strategies to improve early detection and management of the disease. The analysis method used achieved an impressive accuracy rate of approximately 88.8% on the dataset it was applied to. The area under the curve was 0.87, the true positive rate was 88%, and the F1 measure was 0.88. By using accurate diagnostic techniques and providing proactive community care, the incidence of osteoporotic fractures can be significantly reduced, thus improving the quality of life for this vulnerable population. [ABSTRACT FROM AUTHOR]

Machine learning with IoT practices in the agriculture sector has the potential to address numerous challenges encountered by farmers, including disease prediction and estimation of soil profile. This paper extensively explores the classification of diseases in grape plants and provides detailed information about the conducted experiments. It is important to keep track of each crop's current environmental conditions because different environmental conditions, such as humidity, temperature, moisture, leaf wetness, light intensity, wind speed, and wind direction, can affect or sustain the quality of a crop. IoT will increasingly be used in precision agriculture and smart environments to detect, gather, and share data about environmental occurrences. The environmental factor that is active at all times and has an effect on a crop from its cultivation to harvest. With the aid of an IoT, we will monitor the following factors: temperature, humidity, and leaf wetness, all of which have an impact on the overall quality and lifespan of grapes. A Self-created database of weather parameter using sensors is introduced in this article. It consists of 5 categories with a total of 10,000 records. Here, experiment has been carried out using our dataset to predict grape diseases on various machines learning algorithm. The system receives overall accuracy of 98.25 % for Powdery Mildew, 98.85 % for Downy Mildew and 93.95 % for Bacterial Leaf Spot.

With the rapid progress of artificial intelligence (AI) technology in medical scenarios, it becomes a trend for medical services to adopt various AI algorithms for auxiliary diagnosis and health care of patients. However, medical data is often sensitive and possibly owned by multiple participants without the willingness of data sharing. To solve this problem under the vertical partition scenario of medical data, a differentially private distributed collaborative XGBoost method named DP-DCXGBoost was proposed and applied for disease classification in the paper. Initially, a reputation-based participant selection algorithm was designed, which evaluated the contribution of participants to the global model and used it for reputation calculation to select proper participants. Then, in the collaborative training phase, the proposed method utilized the local vertical dataset of each participant to calculate feature buckets and splitting gains in order to collaboratively construct a differentially private global XGBoost classification model. Finally, the experimental analysis for two real disease datasets showed that the proposed method had good classification accuracy on the basis of preserving participants' data privacy.

Diseases affecting potato leaves frequently lead to significant setbacks for farmers, reducing the overall harvest and the quality of the potatoes. Given the critical need for prompt disease detection, this research introduces the use of the MobileNet framework grounded in the Convolutional Neural Network (CNN) for adept detection of potato leaf ailments. During the research, potato leaf images undergo processing, and their distinct features are gleaned using CNN. Then, harnessing the MobileNet framework, these images undergo classification to ascertain the existence of diseases. The aspiration is that the formulated model can pinpoint diseases with notable precision, rapid feedback, and enhanced computational adeptness. Initial findings underscore the potential of this methodology in discerning potato leaf diseases, providing renewed optimism for farmers grappling with plant health issues. Experiments using the Transfer Learning approach showed good performance in classification and displayed a high accuracy rate of 99.2%.

Optimizing cannabis crop yield and quality necessitates accurate, automated leaf disease classi-fication systems for timely detection and intervention. Existing automated solutions, however, are insufficiently tailored to the specific challenges of cannabis disease identification, struggling with robustness across varied environmental conditions and plant growth stages. This paper introduces a novel Hybrid Adaptive Multi-Intelligence System for Deep Learning Ensembles (HyAMIS-DLE), utilizing a comprehensive dataset reflective of the diversity in cannabis leaf diseases and their progression. Our approach combines non-population-based decision fusion in image prepro-cessing with population-based decision fusion in classification, employing multiple CNN archi-tectures. This integration facilitates a significant improvement in performance metrics: Hy-AMIS-DLE achieves an accuracy of 99.58 %, outperforming conventional models by up to 4.16 %, and exhibits superior robustness and an enhanced Area Under the Curve (AUC) score, effectively distinguishing between healthy and diseased leaves. The successful deployment of HyAMIS-DLE within our Automated Cannabis Leaf Disease Classification System (A-CLDC-S) demonstrates its practical value, contributing to increased crop yields, reduced losses, and the promotion of sus-tainable agricultural practices.

Plant disease (PD) detection is a substantial problem that needs to be tackled to develop the economy and improve agricultural production. Using conventional methods to classify plant leaf diseases consumes more time, undergoes vanishing gradients problems, overfitting issues, etc. However, automatic PD detection using deep learning (DL) has attained great significance in detecting PD during the early stages. Therefore, this paper proposes a hybrid strategy based on optimized automatic DL for plant leaf disease classification (PLDC). Initially, the proposed model performs pre-processing using image resizing and Gaussian filtering. Then, the disease infected region is then segmented using the UNet technique to acquire the relevant region and enhance disease classification accuracy. During segmentation, the weight of the UNet model has been tuned by employing the hunter-prey optimization (Hunt-PO) algorithm. Next, feature extraction is accomplished by means of a gray level co-occurrence matrix (GLCM), scale-invariant feature transform (SIFT) and a Gabor filter to extract the crucial features for classification. Further, based on the extracted features, PLDC is performed using artificial driving-EfficientNet (AD-ENet). The proposed PLDC model is implemented in the python platform through the PlantVillage dataset and assessed the performance in terms of different evaluation measures. Moreover, a proposed model's performance is compared with existing classifiers. The maximum classification accuracy obtained by the proposed PLDC model is 99.91%, superior to the existing classifiers for leaf disease classification. [ABSTRACT FROM AUTHOR]

With the rapid progress of artificial intelligence (AI) technology in medical scenarios, it becomes a trend for medical services to adopt various AI algorithms for auxiliary diagnosis and health care of patients. However, medical data is often sensitive and possibly owned by multiple participants without the willingness of data sharing. To solve this problem under the vertical partition scenario of medical data, a differentially private distributed collaborative XGBoost method named DP-DCXGBoost was proposed and applied for disease classification in the paper. Initially, a reputation-based participant selection algorithm was designed, which evaluated the contribution of participants to the global model and used it for reputation calculation to select proper participants. Then, in the collaborative training phase, the proposed method utilized the local vertical dataset of each participant to calculate feature buckets and splitting gains in order to collaboratively construct a differentially private global XGBoost classification model. Finally, the experimental analysis for two real disease datasets showed that the proposed method had good classification accuracy on the basis of preserving participants' data privacy. [ABSTRACT FROM AUTHOR]

Accurate and rapid plant disease detection is critical for enhancing long-term agricultural yield. Disease infection poses the most significant challenge in crop production, potentially leading to economic losses. Viruses, fungi, bacteria, and other infectious organisms can affect numerous plant parts, including roots, stems, and leaves. Traditional techniques for plant disease detection are timeconsuming, require expertise, and are resource-intensive. Therefore, automated leaf disease diagnosis using artificial intelligence (AI) with Internet of Things (IoT) sensors methodologies are considered for the analysis and detection. This research examines four crop diseases: tomato, chilli, potato, and cucumber. It also highlights the most prevalent diseases and infections in these four types of vegetables, along with their symptoms. This review provides detailed predetermined steps to predict plant diseases using AI. Predetermined steps include image acquisition, preprocessing, segmentation, feature selection, and classification. Machine learning (ML) and deep understanding (DL) detection models are discussed. A comprehensive examination of various existing ML and DL-based studies to detect the disease of the following four crops is discussed, including the datasets used to evaluate these studies. We also provided the list of plant disease detection datasets. Finally, different ML and DL application problems are identified and discussed, along with future research prospects, by combining AI with IoT platforms like smart drones for field-based disease detection and monitoring. This work will help other practitioners in surveying different plant disease detection strategies and the limits of present systems. [ABSTRACT FROM AUTHOR]

Objective To investigate the classification and distribution features of orbital space-occupying diseases in children. Methods A retrospective case series study was conducted. The clinical data of 287 pediatric patients with orbital space-occupying diseases admitted to the Department of Ophthalmology at the Second Affiliated Hospital of the Air Force Military Medical University from January 2012 to June 2022 were collected. According to the Shields classification method, the benign and malignant orbital diseases were classified by gender and age, and the classification and distribution features of these diseases were analyzed. Results Among 287 children with orbital space-occupying diseases, 91.29% were benign diseases and 8.71% were malignant diseases. Benign diseases mainly included cystic lesions, vascular lesions, neurogenic tumors, among which dermoid cysts were the most common. The malignant diseases mainly consisted of myogenic tumors and lymphohematopoietic system tumors, among which rhabdomyosarcoma was the most common. Orbital space-occupying lesions in children were mostly found in 0-4 years old, and the incidence rate of dermoid cysts and capillary hemangiomas was significantly correlated with age (P<0.05). The correlation between the nature of the lesion and gender is not significant, however, Langerhans cell histiocytosis, dermoid cysts, capillary hemangiomas, and rhabdomyosarcoma were more common in male patients. Conclusions The clinical distribution pattern of orbital space-occupying diseases in children is different from that observed in adults, it is mainly composed of congenital and embryonic diseases. Benign lesions are predominantly characerized by dermatoid cysts while rhabdomyosarcoma prevails among malignant cases. Certain diseases exhibit distinct age or gender predilections. [ABSTRACT FROM AUTHOR]

In recent times, there has been active research on multi-disease classification that aim to diagnose lung diseases and respiratory conditions using respiratory data. Recorded respiratory data can be used to diagnose various chronic diseases, such as asthma and pneumonia by applying different feature extraction methods. Previous studies have primarily focused on respiratory disease classification using 2D image conversion techniques, such as spectrograms and mel frequency cepstral coefficients (MFCC) for respiratory data. However, as the number of respiratory disease classes increased, the classification accuracy tended to decrease. To address this challenge, this study proposes a novel approach that combines 1D and 2D data to enhance the multi-classification performance regarding respiratory disease. We incorporated widely used 2D representations such as spectrograms, gammatone-based spectrograms, and MFCC images, along with raw data. The proposed respiratory disease classification method comprises 2D data conversion, combined data generation, classification model development, and multi-disease classification steps. Our method achieved high classification accuracies of 92.93%, 91.30%, and 88.58% using the TCN, Wavenet, and BiLSTM models, respectively. Compared to using solely 1D data, our approach demonstrated a 4.89% improvement in accuracy and more than 3 times better training speed when using only 2D data. These results confirmed the superiority of the proposed method. This allows us to leverage the advantages of fast learning provided by time-series models, as well as the high classification accuracy demonstrated by 2D image approaches. [ABSTRACT FROM AUTHOR]

Retinal disorders like diabetic retinopathy pose a significant threat to global vision. Early diagnosis is crucial, and fundus images provide vital insights into retinal conditions, focusing on blood vessel characteristics. Manual retinal vessel segmentation, though precise, is time-consuming and dependent on skilled professionals. Addressing this, an automatic and efficient retinal vessel segmentation method is urgently needed, utilizing computer vision techniques. Existing approaches include machine learning, filtering-based, and model-based methods. Our research aims to evaluate automated segmentation and classification techniques for diabetic retinopathy and glaucoma using diverse retinal image datasets, including DRIVE, REVIEW, STARE, HRF, and DRION. The methodologies under consideration encompass machine learning, filtering-based, and model-based approaches, with performance assessment based on a range of metrics, including true positive rate, true negative rate, positive predictive value, negative predictive value, false discovery rate, Matthews's correlation coefficient, and accuracy. The primary objective of this research is to scrutinize, assess, and compare the design and performance of different segmentation and classification techniques, encompassing both supervised and unsupervised learning methods. To attain this objective, we will refine existing techniques and develop new ones, ensuring a more streamlined and computationally efficient approach. [ABSTRACT FROM AUTHOR]

Early blight and late blight are two of the most prevalent and severe diseases affecting potato crops. Efficient and accurate grading of their severity is crucial for effective disease management. However, existing grading methods are limited to assessing the severity of each disease independently, often resulting in low recognition accuracy and slow grading processes. To address these challenges, this study proposes a novel two-stage approach for the rapid severity grading of both early blight and late blight in potato plants. In this research, two lightweight models were developed: Coformer and SegCoformer. In the initial stage, Coformer efficiently categorizes potato leaves into three classes: those afflicted by early blight, those afflicted by late blight, and healthy leaves. In the subsequent stage, SegCoformer accurately segments leaves, lesions, and backgrounds within the images obtained from the first stage. Furthermore, it assigns severity labels to the identified leaf lesions. To validate the accuracy and processing speed of the proposed methods, we conduct experimental comparisons. The experimental results indicate that Coformer achieves a classification accuracy as high as 97.86%, while SegCoformer achieves an mIoU of 88.50% for semantic segmentation. The combined accuracy of this method reaches 84%, outperforming the Sit + Unet_V accuracy by 1%. Notably, this approach achieves heightened accuracy while maintaining a faster processing speed, completing image processing in just 258.26 ms. This research methodology effectively enhances agricultural production efficiency. [ABSTRACT FROM AUTHOR]

Introduction: Tomato leaf diseases can cause major yield and quality losses. Computer vision techniques for automated disease recognition show promise but face challenges like symptom variations, limited labeled data, and model complexity. Methods: Prior works explored hand-crafted and deep learning features for tomato disease classification and multi-task severity prediction, but did not sufficiently exploit the shared and unique knowledge between these tasks. We present a novel multi-task distillation learning (MTDL) framework for comprehensive diagnosis of tomato leaf diseases. It employs knowledge disentanglement, mutual learning, and knowledge integration through a multistage strategy to leverage the complementary nature of classification and severity prediction. Results: Experiments show our framework improves performance while reducing model complexity. The MTDL-optimized EfficientNet outperforms single-task ResNet101 in classification accuracy by 0.68% and severity estimation by 1.52%, using only 9.46% of its parameters. Discussion: The findings demonstrate the practical potential of our framework for intelligent agriculture applications. [ABSTRACT FROM AUTHOR]

Agricultural products are a fundamental necessity for every country. When plants are afflicted with diseases, it influences the country's agricultural productivity, as well as its economic resources. Diseases are an important problem for potato plants, causing potatoes to be rejected and resulting in financial losses. Viruses and diseases in potatoes and field plants can be missed with the naked eye, particularly in the early stages of cultivation. The use of modern instruments and technology at an early stage of disease diagnosis dramatically reduces costs. This study used deep learning techniques to categorize and detect plant leaf diseases in photos taken from the Plant Village dataset. The dataset consists of 20,636 photos of plants and their diseases. This study focused on potato plants because it is the most common type of plant in the world, particularly in Pakistan. Convolutional Neural Network (CNN) methods were used to categorize plant leaf diseases into 15 classes, including three classes for healthy leaves and classes for several plant diseases such as fungal and bacterial infections, among others. The proposed models were trained and tested, achieving 98.29 and 98.029% accuracy, respectively. [ABSTRACT FROM AUTHOR]

Abstract Background Deep‐learning (DL) methods are rapidly changing the way researchers classify neurological disorders. For example, combining functional magnetic resonance imaging (fMRI) and DL has helped researchers identify functional biomarkers of neurological disorders (e.g., brain activation and connectivity) and pilot innovative diagnostic models. However, the knowledge required to perform DL analyses is often domain‐specific and is not widely taught in the brain sciences (e.g., psychology, neuroscience, and cognitive science). Conversely, neurological diagnoses and neuroimaging training (e.g., fMRI) are largely restricted to the brain and medical sciences. In turn, these disciplinary knowledge barriers and distinct specializations can act as hurdles that prevent the combination of fMRI and DL pipelines. The complexity of fMRI and DL methods also hinders their clinical adoption and generalization to real‐world diagnoses. For example, most current models are not designed for clinical settings or use by nonspecialized populations such as students, clinicians, and healthcare workers. Accordingly, there is a growing area of assistive tools (e.g., software and programming packages) that aim to streamline and increase the accessibility of fMRI and DL pipelines for the diagnoses of neurological disorders. Objectives and Methods In this study, we present an introductory guide to some popular DL and fMRI assistive tools. We also create an example autism spectrum disorder (ASD) classification model using assistive tools (e.g., Optuna, GIFT, and the ABIDE preprocessed repository), fMRI, and a convolutional neural network. Results In turn, we provide researchers with a guide to assistive tools and give an example of a streamlined fMRI and DL pipeline. Conclusions We are confident that this study can help more researchers enter the field and create accessible fMRI and deep‐learning diagnostic models for neurological disorders.

Machine learning (ML) can enhance agricultural yields by combating plant diseases and climate change. However, traditional image processing techniques for disease detection have limitations in robustness and generalizability. In this study, we investigate deep transfer learning for fine-grained disease classification in maize plants, which is a challenging task due to the subtle and nuanced disease patterns. We use four tailored deep learning frameworks: VGGNET, Inception V3, ResNet50, and InceptionResNetV2. ResNet50 achieves the highest validation accuracy of 87.51%, precision of 90.33%, and recall of 99.80%, demonstrating the efficacy and superiority of our approach. Our study offers an innovative solution for accurate disease classification in maize plants.

Dementia is a condition that often comes with aging and affects how people think, remember, and behave. Diagnosing dementia early is important because it can greatly improve patients’ lives. This systematic review looks at how deep learning (DL) techniques have been used to diagnose dementia automatically from 2012 to 2023. We explore how different DL methods like Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Deep Neural Networks (DNN) are used to diagnose types of dementia such as Alzheimer’s, vascular dementia, and Lewy body dementia. We also discuss the difficulties of using DL for diagnosing dementia, like the lack of large and varied datasets and the challenge of applying models to different groups of people. These issues indicate the need for more dependable and understandable models that consider a wide range of patient characteristics and biomarkers. Longitudinal studies are also needed to understand how the disease progresses and how treatments work. Collaboration among researchers, doctors, and data scientists is crucial to ensure DL models are scientifically sound and effective in clinical settings. In summary, DL techniques show promise for automated dementia diagnosis and could improve how accurately and efficiently it is diagnosed in practice. However, further research is needed to address the challenges highlighted in this review.

The rapid and accurate classification of Panax notoginseng leaf diseases is vital for timely disease control and reducing economic losses. Recently, image classification algorithms have shown great promise for plant disease diagnosis, but dataset quantity and quality are crucial. Moreover, classifying P. notoginseng leaf diseases faces severe challenges, including the small features of anthrax and the strong similarity between round spot and melasma diseases. In order to address these problems, we have proposed an ECA-based diffusion model and Inception-SSNet for the classification of the six major P. notoginseng leaf diseases, namely gray mold, powdery mildew, virus infection, anthrax, melasma, and round spot. Specifically, we propose an image generation scheme, in which the lightweight attention mechanism, ECA, is used to capture the dependencies between channels for improving the dataset quantity and quality. To extract disease features more accurately, we developed an Inception-SSNet hybrid model with skip connection, attention feature fusion, and self-calibrated convolutional. These innovative methods enable the model to make better use of local and global information, especially when dealing with diseases with similar features and small targets. The experimental results show that our proposed ECA-based diffusion model FID reaches 42.73, compared with the baseline model, which improved by 74.71%. Further, we tested the classification model using the data set of P. notoginseng leaf disease generation, and the accuracy of 11 mainstream classification models was improved. Our proposed Inception-SSNet classification model achieves an accuracy of 97.04% on the non-generated dataset, which is an improvement of 0.11% compared with the baseline model. On the generated dataset, the accuracy reached 99.44%, which is an improvement of 1.02% compared to the baseline model. This study provides an effective solution for the monitoring of Panax notoginseng diseases.