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Due to the progress in image processing and Artificial Intelligence (AI), it is now possible to develop automated tool for the early detection and diagnosis of Alzheimer’s Disease (AD). Handcrafted techniques developed so far, lack generality, leading to the development of deep learning (DL) techniques, which can extract more relevant features. To cater for the limited labelled datasets and requirement in terms of high computational power, transfer learning models can be adopted as a baseline. In recent years, considerable research efforts have been devoted to developing machine learning-based techniques for AD detection and classification using medical imaging data. This survey paper comprehensively reviews the existing literature on various methodologies and approaches employed for AD detection and classification, with a focus on neuroimaging techniques such as structural MRI, PET, and fMRI. The main objective of this survey is to analyse the different transfer learning models that can be used for the deployment of deep convolution neural network for AD detection and classification. The phases involved in the development namely image capture, pre-processing, feature extraction and selection are also discussed in the view of shedding light on the different phases and challenges that need to be addressed. The research perspectives may provide research directions on the development of automated applications for AD detection and classification. [ABSTRACT FROM AUTHOR]

Lung cancer, the second most common type of cancer worldwide, presents significant health challenges. Detecting this disease early is essential for improving patient outcomes and simplifying treatment. In this study, we propose a hybrid framework that combines deep learning (DL) with quantum computing to enhance the accuracy of lung cancer detection using chest radiographs (CXR) and computerized tomography (CT) images. Our system utilizes pre-trained models for feature extraction and quantum circuits for classification, achieving state-of-the-art performance in various metrics. Not only does our system achieve an overall accuracy of 92.12%, it also excels in other crucial performance measures, such as sensitivity (94%), specificity (90%), F1-score (93%), and precision (92%). These results demonstrate that our hybrid approach can more accurately identify lung cancer signatures compared to traditional methods. Moreover, the incorporation of quantum computing enhances processing speed and scalability, making our system a promising tool for early lung cancer screening and diagnosis. By leveraging the strengths of quantum computing, our approach surpasses traditional methods in terms of speed, accuracy, and efficiency. This study highlights the potential of hybrid computational technologies to transform early cancer detection, paving the way for wider clinical applications and improved patient care outcomes. [ABSTRACT FROM AUTHOR]

IntroductionParkinson’s disease (PD) is a neurodegenerative illness that impairs normal human movement. The primary cause of PD is the deficiency of dopamine in the human brain. PD also leads to several other challenges, including insomnia, eating disturbances, excessive sleepiness, fluctuations in blood pressure, sexual dysfunction, and other issues.MethodsThe suggested system is an extremely promising technological strategy that may help medical professionals provide accurate and unbiased disease diagnoses. This is accomplished by utilizing significant and unique traits taken from spiral drawings connected to Parkinson’s disease. While PD cannot be cured, early administration of drugs may significantly improve the condition of a patient with PD. An expeditious and accurate clinical classification of PD ensures that efficacious therapeutic interventions can commence promptly, potentially impeding the advancement of the disease and enhancing the quality of life for both patients and their caregivers. Transfer learning models have been applied to diagnose PD by analyzing important and distinctive characteristics extracted from hand-drawn spirals. The studies were carried out in conjunction with a comparison analysis employing 102 spiral drawings. This work enhances current research by analyzing the effectiveness of transfer learning models, including VGG19, InceptionV3, ResNet50v2, and DenseNet169, for identifying PD using hand-drawn spirals.ResultsTransfer machine learning models demonstrate highly encouraging outcomes in providing a precise and reliable classification of PD. Actual results demonstrate that the InceptionV3 model achieved a high accuracy of 89% when learning from spiral drawing images and had a superior receiver operating characteristic (ROC) curve value of 95%.DiscussionThe comparison results suggest that PD identification using these models is currently at the forefront of PD research. The dataset will be enlarged, transfer learning strategies will be investigated, and the system’s integration into a comprehensive Parkinson’s monitoring and evaluation platform will be looked into as future research areas. The results of this study could lead to a better quality of life for Parkinson’s sufferers, individualized treatment, and an early classification.

This study focuses on efficiently adapting transfer learning models to address the challenges of creating customized deep learning models for specific datasets. Designing a model from scratch can be time-consuming and complex due to factors like model complexity, size, and dataset structure. To overcome these obstacles, a novel approach is proposed using transfer learning models. The proposed method involves identifying relevant layers in transfer learning models and removing unnecessary ones using a layer-based variance pruning technique. This results in the creation of new models with improved computational efficiency and classification performance. By streamlining the models through layer-based variance pruning, the study achieves enhanced accuracy and faster computation. Experiments were conducted using the COVID-19 dataset and well-known transfer learning models, including InceptionV3, ResNet50V2, DenseNet201, VGG16, and Xception to validate the approach. Among these models, the variance-based layer pruning technique was applied to InceptionV3 and DenseNet201, yielding the best results. When these pruned models were combined with the new pooling layer, Avg-TopK, the proposed method achieved an outstanding image classification accuracy of 99.3%. Comparisons with previous models and literature studies indicate that the proposed approach outperforms existing methods, showcasing state-of-the-art performance. This high-performance approach provides great potential for diagnosing COVID-19 and monitoring disease progression, especially on hardware-limited devices. By leveraging transfer learning models, pruning, and efficient pooling techniques, the study presents a promising strategy for tackling challenges in custom model design, leading to exceptional results in such as image classification and segmentation tasks. The proposed methodology holds the potential to yield exceptional outcomes across a spectrum of tasks, encompassing disciplines such as image classification and segmentation. [ABSTRACT FROM AUTHOR]

The human skin serves as a critical indicator of underlying health conditions, often manifesting early signs of disorders affecting internal organs. Recognizing these signs is crucial for timely diagnosis and treatment. However, the significance of the body’s natural defence mechanism through the skin is sometimes overlooked. This study aims to develop a skin disease classification system using a multimodal approach, integrating ensemble methodology using DenseNet169 – Resnet50 ensemble Transfer learning models, and Natural Language Processing (NLP) within a Telegram chatbot interface. The primary aim is to enhance the chatbot’s ability to deliver customised skin-related diagnoses by using user-provided information, including skin type, chemical exposure, and previous treatments, ensuring more precise and personalised interactions. Additionally, the accuracy and generalization capability of the classification system is improved by analysing the data from both the chatbot and image analysis. The chatbot’s self-learning capabilities allow it to improve its comprehension over time in response to user input, which makes it more adept at personalising queries. Integrating DenseNet169 and ResNet50 strengthens the feature reuse by connecting each layer to every other layer in a dense manner for efficient gradient flow thus reducing the number of parameters. ResNet50, with its residual connections, helps mitigate vanishing gradient issues, allowing for deeper networks with stable training leading to improved feature extraction and representation. This hybrid approach captures fine-grained global features enhancing the feature learning attaining good performance in complex tasks. A total of 11,747 images were evaluated in the proposed study, with 7,930 images allocated for training and validation, and 3,817 images designated for testing. The model attained an accuracy of 77.07% and an AUC score of 96.72%. The NLP training model achieved an accuracy of 93.62% ensuring a comprehensive understanding of user data and accelerating engagement, leading to more precise and personalized predictions of skin disorders.

Billions of people all over the globe suffer from osteoporosis, the second most prevalent bone condition after arthritis. Osteoporosis is brought on by a decline in bone mineral density, which can cause discomfort, deformity, injuries, and, in extreme instances, fatalities. Although DXA is used to diagnose it, its high price, limited availability, and erratic BMD values make it unreliable. The diagnoses have significantly improved owing to computer-aided diagnosis. A precise diagnosis of the condition may be made using deep learning-based networks, which have demonstrated cutting-edge outcomes in the diagnostic sector. Under the transfer learning methodology, this research investigates the performance of known neural network designs with the objective of osteoporosis diagnosis. The ability to distinguish between x-rays of healthy individuals and those obtained from individuals with osteoporosis has been tested using eight well-known ImageNet pre-trained models to provide a thorough comparison. 372 X-rays, divided into training and test sets, in the investigations. Standard evaluation parameters (such as accuracy, precision, recall and f1-score) have been calculated for all architectures, and the majority of designs performed significantly well, with the highest achieving an average accuracy of up to 86.36% when distinguishing the specified classes. [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy threatens vision in diabetics, necessitating swift and accurate detection. This study employs Convolutional Neural Network (CNN), ResNet50, and InceptionV3 for automatic DR identification, achieving a notable 96.18% accuracy over 80 epochs. To enhance robustness, a pre-processing pipeline incorporates Gaussian filtering, CLAHE, median filtering, and top-hat filtering, significantly advancing DR detection accuracy. Evaluation on the APTOS 2019 dataset (1299 training, 279 testing images) reveals great accuracy as well as sensitivity, and specificity, forming a basis for early intervention and vision impairment prevention. This research at the nexus of DL which is also known as deep learning and medical image analyze offers a promising solution for early DR detection. The 96.18% accuracy demonstrates practical viability, positioning our approach as a valuable tool for healthcare practitioners and ophthalmologists in effectively diagnosing and managing diabetic retinopathy. [ABSTRACT FROM AUTHOR]

Heart disease can be life-threatening if not detected and treated at an early stage. The electrocardiogram (ECG) plays a vital role in classifying cardiovascular diseases, and often physicians and medical researchers examine paper-based ECG images for cardiac diagnosis. An automated heart disease prediction system might help to classify heart diseases accurately at an early stage. This study aims to classify cardiac diseases into five classes with paper-based ECG images using a deep learning approach with the highest possible accuracy and the lowest possible time complexity. This research consists of two approaches. In the first approach, five deep learning models, InceptionV3, ResNet50, MobileNetV2, VGG19, and DenseNet201, are employed. In the second approach, an integrated deep learning model (InRes-106) is introduced, combining InceptionV3 and ResNet50. This model is developed as a deep convolutional neural network capable of extracting hidden and high-level features from images. An ablation study is conducted on the proposed model altering several components and hyperparameters, improving the performance even further. Before training the model, several image pre-processing techniques are employed to remove artifacts and enhance the image quality. Our proposed hybrid InRes-106 model performed best with a testing accuracy of 98.34%. The InceptionV3 model acquired a testing accuracy of 90.56%, the ResNet50 89.63%, the DenseNet201 88.94%, the VGG19 87.87%, and the MobileNetV2 achieved 80.56% testing accuracy. The model is trained with a k-fold cross-validation technique with different k values to evaluate the robustness further. Although the dataset contains a limited number of complex ECG images, our proposed approach, based on various image pre-processing techniques, model fine-tuning, and ablation studies, can effectively diagnose cardiac diseases. [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy (DR) is an eye condition that mainly affects individuals who have diabetes and is one of the important causes of blindness in adults. As the infection progresses, it may lead to permanent loss of vision. Diagnosing diabetic retinopathy manually with the help of an ophthalmologist has been a tedious and a very laborious procedure. This paper not only focuses on diabetic retinopathy detection but also on the analysis of different DR stages, which is performed with the help of Deep Learning (DL) and transfer learning algorithms. CNN, hybrid CNN with ResNet, hybrid CNN with DenseNet are used on a huge dataset with around 3662 train images to automatically detect which stage DR has progressed. Five DR stages, which are 0 (No DR), 1 (Mild DR), 2 (Moderate), 3 (Severe) and 4 (Proliferative DR) are processed in the proposed work. The patient's eye images are fed as input to the model. The proposed deep learning architectures like CNN, hybrid CNN with ResNet, hybrid CNN with DenseNet 2.1 are used to extract the features of the eye for effective classification. The models achieved an accuracy of 96.22%, 93.18% and 75.61% respectively. The paper concludes with a comparative study of the CNN, hybrid CNN with ResNet, hybrid CNN with DenseNet architectures that highlights hybrid CNN with DenseNet as the perfect deep learning classification model for automated DR detection. [ABSTRACT FROM AUTHOR]

People of all countries, developed and developing alike endure cancer-related fatal diseases. The rate of breast cancer in females is increasing daily, partly due to ignorance and misdiagnosis in the early stages. Diagnosis of breast cancer accurately during its earlier stages of development can result in proper initial treatment for breast cancer. Artificial intelligence can aid in the acceleration and automation of breast cancer detection. Deep learning is decisive in effectively recognizing and classifying cancer on large datasets of medical images. In this paper, we propose a novel computer-aided classification approach, Mammo-Light for breast cancer prediction. Preprocessing strategies have been utilized to eradicate the noise and enhance mammogram lesions. Photometric augmentation techniques adapted to the preprocessed classes to balance and increase the size of the dataset. After that, a lightweight yet intuitive convolutional neural network is applied to classify breast cancer on the publicly available dataset CBIS-DDSM. For further validation of the proposed approach, we have used the MIAS dataset. Mammo-Light attained a 99.17% and 98.42% test accuracy respectively for CBIS-DDSM and MIAS datasets and outperformed state-of-the-art methods in terms of accuracy and other metrics. Due to being the lightweight model, Mammo-Light performs exceptionally well with fewer parameters and computational time, which can potentially contribute to the field of breast cancer early diagnosis and enable fast treatment. • Effective preprocessed procedures for removing noise and enhancing mammograms. • Balance and expand the data in a highly efficient manner. • A robust and high-accuracy lightweight deep-learning model contains only a few layers proposed. [ABSTRACT FROM AUTHOR]

Heart disease can be life-threatening if not detected and treated at an early stage. The electrocardiogram (ECG) plays a vital role in classifying cardiovascular diseases, and often physicians and medical researchers examine paper-based ECG images for cardiac diagnosis. An automated heart disease prediction system might help to classify heart diseases accurately at an early stage. This study aims to classify cardiac diseases into five classes with paper-based ECG images using a deep learning approach with the highest possible accuracy and the lowest possible time complexity. This research consists of two approaches. In the first approach, five deep learning models, InceptionV3, ResNet50, MobileNetV2, VGG19, and DenseNet201, are employed. In the second approach, an integrated deep learning model (InRes-106) is introduced, combining InceptionV3 and ResNet50. This model is developed as a deep convolutional neural network capable of extracting hidden and high-level features from images. An ablation study is conducted on the proposed model altering several components and hyperparameters, improving the performance even further. Before training the model, several image pre-processing techniques are employed to remove artifacts and enhance the image quality. Our proposed hybrid InRes-106 model performed best with a testing accuracy of 98.34%. The InceptionV3 model acquired a testing accuracy of 90.56%, the ResNet50 89.63%, the DenseNet201 88.94%, the VGG19 87.87%, and the MobileNetV2 achieved 80.56% testing accuracy. The model is trained with a k-fold cross-validation technique with different k values to evaluate the robustness further. Although the dataset contains a limited number of complex ECG images, our proposed approach, based on various image pre-processing techniques, model fine-tuning, and ablation studies, can effectively diagnose cardiac diseases.

Acute Lymphoblastic Leukemia (ALL) is cancer in which bone marrow overproduces undeveloped lymphocytes. Over 6500 cases of ALL are diagnosed every year in the United States in both adults and children, accounting for around 25% of pediatric cancers, and the trend continues to rise. With the advancements of AI and big data analytics, early diagnosis of ALL can be used to aid the clinical decisions of physicians and radiologists. This research proposes a deep neural network-based (ALNett) model that employs depth-wise convolution with different dilation rates to classify microscopic white blood cell images. Specifically, the cluster layers encompass convolution and max-pooling followed by a normalization process that provides enriched structural and contextual details to extract robust local and global features from the microscopic images for the accurate prediction of ALL. The performance of the model was compared with various pre-trained models, including VGG16, ResNet-50, GoogleNet, and AlexNet, based on precision, recall, accuracy, F1 score, loss accuracy, and receiver operating characteristic (ROC) curves. Experimental results showed that the proposed ALNett model yielded the highest classification accuracy of 91.13% and an F1 score of 0.96 with less computational complexity. ALNett demonstrated promising ALL categorization and outperformed the other pre-trained models., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Simple Summary: Breast cancer diagnosis at an early stage using mammography is important, as it assists clinical specialists in treatment planning to increase survival rates. The aim of this study is to construct an effective method to classify breast images into four classes with a low error rate. Initially, unwanted regions of mammograms are removed, the quality is enhanced, and the cancerous lesions are highlighted with different artifacts removal, noise reduction, and enhancement techniques. The number of mammograms is increased using seven augmentation techniques to deal with over-fitting and under-fitting problems. Afterwards, six fine-tuned convolution neural networks (CNNs), originally developed for other purposes, are evaluated, and VGG16 yielded the highest performance. We propose a BreastNet18 model based on the fine-tuned VGG16, changing different hyper parameters and layer structures after experimentation with our dataset. Performing an ablation study on the proposed model and selecting suitable parameter values for preprocessing algorithms increases the accuracy of our model to 98.02%, outperforming some existing state-of-the-art approaches. To analyze the performance, several performance metrics are generated and evaluated for every model and for BreastNet18. Results suggest that accuracy improvement can be obtained through image pre-processing techniques, augmentation, and ablation study. To investigate possible overfitting issues, a k-fold cross validation is carried out. To assert the robustness of the network, the model is tested on a dataset containing noisy mammograms. This may help medical specialists in efficient and accurate diagnosis and early treatment planning. Background: Identification and treatment of breast cancer at an early stage can reduce mortality. Currently, mammography is the most widely used effective imaging technique in breast cancer detection. However, an erroneous mammogram based interpretation may result in false diagnosis rate, as distinguishing cancerous masses from adjacent tissue is often complex and error-prone. Methods: Six pre-trained and fine-tuned deep CNN architectures: VGG16, VGG19, MobileNetV2, ResNet50, DenseNet201, and InceptionV3 are evaluated to determine which model yields the best performance. We propose a BreastNet18 model using VGG16 as foundational base, since VGG16 performs with the highest accuracy. An ablation study is performed on BreastNet18, to evaluate its robustness and achieve the highest possible accuracy. Various image processing techniques with suitable parameter values are employed to remove artefacts and increase the image quality. A total dataset of 1442 preprocessed mammograms was augmented using seven augmentation techniques, resulting in a dataset of 11,536 images. To investigate possible overfitting issues, a k-fold cross validation is carried out. The model was then tested on noisy mammograms to evaluate its robustness. Results were compared with previous studies. Results: Proposed BreastNet18 model performed best with a training accuracy of 96.72%, a validating accuracy of 97.91%, and a test accuracy of 98.02%. In contrast to this, VGGNet19 yielded test accuracy of 96.24%, MobileNetV2 77.84%, ResNet50 79.98%, DenseNet201 86.92%, and InceptionV3 76.87%. Conclusions: Our proposed approach based on image processing, transfer learning, fine-tuning, and ablation study has demonstrated a high correct breast cancer classification while dealing with a limited number of complex medical images. [ABSTRACT FROM AUTHOR]