Search

Deep learning-based segmentation models often struggle to achieve optimal performance when encountering new, unseen semantic classes. Their effectiveness hinges on vast amounts of annotated data and high computational resources for training. However, a promising solution to mitigate these challenges is the adoption of few-shot segmentation (FSS) networks, which can train models with reduced annotated data. The inherent complexity of medical images limits the applicability of FSS in medical imaging, despite its potential. Recent advancements in self-supervised label-efficient FSS models have demonstrated remarkable efficacy in medical image segmentation tasks. This paper presents a novel FSS architecture that enhances segmentation accuracy by utilising fewer features than existing methodologies. Additionally, this paper proposes a novel self-supervised learning approach that utilises supervoxel and augmented superpixel images to further enhance segmentation accuracy. This paper assesses the efficacy of the proposed model on two different datasets: abdominal magnetic resonance imaging (MRI) and cardiac MRI. The proposed model achieves a mean dice score and mean intersection over union of 81.62% and 70.38% for abdominal images, and 79.38% and 65.23% for cardiac images. [ABSTRACT FROM AUTHOR]

As deep learning technology has progressed, automated medical image analysis is becoming ever more crucial in clinical diagnosis. However, due to the diversity and complexity of blood cell images, traditional models still exhibit deficiencies in blood cell detection. To address blood cell detection, we developed the TW-YOLO approach, leveraging multi-scale feature fusion techniques. Firstly, traditional CNN (Convolutional Neural Network) convolution has poor recognition capabilities for certain blood cell features, so the RFAConv (Receptive Field Attention Convolution) module was incorporated into the backbone of the model to enhance its capacity to extract geometric characteristics from blood cells. At the same time, utilizing the feature pyramid architecture of YOLO (You Only Look Once), we enhanced the fusion of features at different scales by incorporating the CBAM (Convolutional Block Attention Module) in the detection head and the EMA (Efficient Multi-Scale Attention) module in the neck, thereby improving the recognition ability of blood cells. Additionally, to meet the specific needs of blood cell detection, we designed the PGI-Ghost (Programmable Gradient Information-Ghost) strategy to finely describe the gradient flow throughout the process of extracting features, further improving the model's effectiveness. Experiments on blood cell detection datasets such as BloodCell-Detection-Dataset (BCD) reveal that TW-YOLO outperforms other models by 2%, demonstrating excellent performance in the task of blood cell detection. In addition to advancing blood cell image analysis research, this work offers strong technical support for future automated medical diagnostics. [ABSTRACT FROM AUTHOR]

Several medical consultations and examinations have been undertaken online since the Covid outbreak. However, when private data was communicated over the internet or uploaded to the cloud, medical information became more susceptible to security risks. Steganography is a technique that can be used to hide sensitive information within a cover image. This paper presents four improved algorithms to enhance steganography's performance in medical images. A full-size hidden image that is as huge as a cover image cannot be handled by previous methods, which is what the algorithmic design is meant to address. Several creative methods are presented, including the computation of Discrete Cosine Transform (DCT) coefficients based on scaled floating values, the addition of an adaptive compression matrix, and a new approach for systematically dispersing a concealed number of bits across multiple separate locations in the cover image. The results of the experiment showed a notable advancement over the earlier research. Our secret image size is substantially larger than the past studies, yet the structure similarity index matrix (SSIM) of the best reconstructed secret image is close to ideal, the peak signal-to-noise ratio (PSNR) and the payload capacity are higher than in the previous studies. This research is beneficial since it contributes to a medical application for enhancing the security of information concealed in chest X-ray images. Medical personnel can generate an image that conceals patient information in a secure manner. [ABSTRACT FROM AUTHOR]

The evaluation of carotid ultrasound image plaque requires a large number of experienced clinicians, and the ultrasound image has the characteristics of blurred boundary and strong noise interference, making the evaluation of plaques time-consuming and laborious. Therefore, a fully automated carotid plaque segmentation method is urgently needed to solve the problem of manpower scarcity. This study proposes a deep neural network model based on Swin-Transformer (Shifted-Windows Transformer) block for the automatic segmentation of carotid plaques. Based on the U-Net(U-Convolutional Network) architecture, the encoding part uses three convolutional blocks for image down-sampling to obtain feature images of different resolution sizes, and then adds six pairs of two consecutive Swin-Transformer blocks for more refined feature extraction. The decoding part up-samples the refined features output by the Swin-Transformer module step by step, and jump-joints them with the feature maps of each resolution level in the encoding part, respectively. The comparison experiments based on the data set of Tong Ren Hospital show that the Dice index of the proposed deep neural network model reaches 0.814 2, which is higher than that of other comparison networks. The results demonstrate that the proposed model can effectively extract the features of carotid ultrasound image plaques and achieve automated and high-precision plaque segmentation. [ABSTRACT FROM AUTHOR]

Gastrointestinal (GI) diseases are a major issue in the human digestive system. Therefore, many studies have explored the automatic classification of GI diseases to reduce the burden on clinicians and improve patient outcomes for both diagnosis and treatment purposes. Convolutional neural networks (CNNs) and Vision Transformers (ViTs) in deep learning approaches have become a popular research area for the automatic detection of diseases from medical images. This study evaluated the classification performance of thirteen different CNN models and two different ViT architectures on endoscopic images. The impact of transfer learning parameters on classification performance was also observed. The tests revealed that the classification accuracies of the ViT models were 91.25% and 90.50%, respectively. In contrast, the DenseNet201 architecture, with optimized transfer learning parameters, achieved an accuracy of 93.13%, recall of 93.17%, precision of 93.13%, and an F1 score of 93.11%, making it the most successful model among all the others. Considering the results, it is evident that a well-optimized CNN model achieved better classification performance than the ViT models. [ABSTRACT FROM AUTHOR]

How to guarantee the confidentiality of sensitive data communicated over the Internet and restrict access to designated information is today's key security and protection concern in telemedicine. In this work, we suggest a reliable and blind medical image watermarking method that combines integer wavelet transform (IWT) and singular value decomposition to keep such information private. A major drawback of current IWT-based watermarking systems is their low embedding capacity. This paper suggests an IWT-based secure large capacity watermarking solution to overcome this specific drawback. The proposed technique effectively preserves a considerable quality of watermarked images, and the watermark is resistant to the most frequently used attacks in watermarking, according to experiment results on imperceptibility and robustness. [ABSTRACT FROM AUTHOR]

The International Skin Imaging Collaboration (ISIC) datasets are pivotal resources for researchers in machine learning for medical image analysis, especially in skin cancer detection. These datasets contain tens of thousands of dermoscopic photographs, each accompanied by gold-standard lesion diagnosis metadata. Annual challenges associated with ISIC datasets have spurred significant advancements, with research papers reporting metrics surpassing those of human experts. Skin cancers are categorized into melanoma and non-melanoma types, with melanoma posing a greater threat due to its rapid potential for metastasis if left untreated. This paper aims to address challenges in skin cancer detection via visual inspection and manual examination of skin lesion images, processes historically known for their laboriousness. Despite notable advancements in machine learning and deep learning models, persistent challenges remain, largely due to the intricate nature of skin lesion images. We review research on convolutional neural networks (CNNs) in skin cancer classification and segmentation, identifying issues like data duplication and augmentation problems. We explore the efficacy of Vision Transformers (ViTs) in overcoming these challenges within ISIC dataset processing. ViTs leverage their capabilities to capture both global and local relationships within images, reducing data duplication and enhancing model generalization. Additionally, ViTs alleviate augmentation issues by effectively leveraging original data. Through a thorough examination of ViT-based methodologies, we illustrate their pivotal role in enhancing ISIC image classification and segmentation. This study offers valuable insights for researchers and practitioners looking to utilize ViTs for improved analysis of dermatological images. Furthermore, this paper emphasizes the crucial role of mathematical and computational modeling processes in advancing skin cancer detection methodologies, highlighting their significance in improving algorithmic performance and interpretability. [ABSTRACT FROM AUTHOR]

Content Based Image Retrieval (CBIR) is a relatively new idea in the field of real-time image retrieval applications; it is a framework for retrieving pictures from diverse medical imaging sources using a variety of image-related attributes, such as color, texture, and form. Using both single and multiple input queries, CBIR processes semantic data or the same object for various class labels in the context of medical image retrieval. Due to the ambiguity of image search, optimizing the retrieval of a query picture by comparing it across numerous image sources may be problematic. The goal is to find a way to optimize the process by which requested images are retrieved from various storage locations. To effectively extract medical images, we propose a hybrid framework (consisting of deep convolution neural networks (DCNN) and the Pareto Optimization technique). In order to obtain medical pictures, a DCNN is trained on them, and then its properties and classification results are employed. Explore enhanced effective medical picture retrieval by using a Pareto optimization strategy to eliminate superfluous and dominant characteristics. When it comes to retrieving images by query from various picture archives, our method outperforms more conventional methods. Use the jargon of machine learning to propose a Novel Unsupervised Label Indexing (NULI) strategy for retrieving picture labels. To enhance the effectiveness of picture retrieval, we characterize machine learning as a matrix convex optimization using a cluster rebased matrix representation. We describe an empirical investigation on many medical picture datasets, finding that the searchbased image annotation (SBIA) schema benefits from our suggested method. As a result, CT images of the lung region are explored in this study by constructing a content-based image retrieval system using various machine learning and Artificial Intelligence techniques. Real-world applications of medical imaging are becoming more significant. Medical research facilities acquire and archive a wide variety of medical pictures digitally. [ABSTRACT FROM AUTHOR]

Medical image encryption has currently drawn particular attention because of the sensitive nature of medical data, the lack of efficient image encryption employing innovative encryption methods. To enhance the encryption of medical images, a number of encryption approaches were suggested and created. This research proposes a hyperchaotic map as well as a deep learning (DL) based selective medical image encryption technique. Initially, the 3D_Att_ResU-Net segments more sensitive region of interest (ROI) which is the brain tumours. The process continues with a hyperchaotic map for encrypting the ROI of plain medical image. On the other hand, the region of non interest (RONI) is compressed based on discrete cosine transform (DCT). The encrypting of ROI as well as compression of the background could provide the best possible balance between encryption performance and security. The samples for the simulation included a variety of common medical image types as well as a few normal images. The outcomes have been analysed using standard image cryptanalysis techniques. The findings demonstrated that cipher-images had uniformly distributed histogram, high information entropy, low correlation between neighbouring pixels, and good visual quality. The method has a large key space as well as low time complexity, yet it is sensitive to the plain image and the initial key. Interestingly, it has been found that the values of NPCR and UACI are higher than 99.75% and 36. 53%, respectively besides large key space registered as an order of 2339. The entropy of the Region of Interest (ROI) is roughly estimated to be 8, which is significantly represent a better security for the current system. The estimated encryption time required for the ROI of size 8.09 KB is 0.3s and to compress the RONI of size 73.81 is 1.02s while the total time required to encrypt whole image of size 81.9 is 2.4737s making it suitable for real time applications. The results depict that the current presented encryption scheme gives high security with less computational time and complexity. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions Multi‐organ segmentation is a critical task in medical imaging, with wide‐ranging applications in both clinical practice and research. Accurate delineation of organs from high‐resolution 3D medical images, such as CT scans, is essential for radiation therapy planning, enhancing treatment outcomes, and minimizing radiation toxicity risks. Additionally, it plays a pivotal role in quantitative image analysis, supporting various medical research studies. Despite its significance, manual segmentation of multiple organs from 3D images is labor‐intensive and prone to low reproducibility due to high interoperator variability. Recent advancements in deep learning have led to several automated segmentation methods, yet many rely heavily on labeled data and human anatomy expertise.In this study, our primary objective is to address the limitations of existing semi‐supervised learning (SSL) methods for abdominal multi‐organ segmentation. We aim to introduce a novel SSL approach that leverages unlabeled data to enhance the performance of deep neural networks in segmenting abdominal organs. Specifically, we propose a method that incorporates a redrawing network into the segmentation process to correct errors and improve accuracy.Our proposed method comprises three interconnected neural networks: a segmentation network for image segmentation, a teacher network for consistency regularization, and a redrawing network for object redrawing. During training, the segmentation network undergoes two rounds of optimization: basic training and readjustment. We adopt the Mean‐Teacher model as our baseline SSL approach, utilizing labeled and unlabeled data. However, recognizing significant errors in abdominal multi‐organ segmentation using this method alone, we introduce the redrawing network to generate redrawn images based on CT scans, preserving original anatomical information. Our approach is grounded in the generative process hypothesis, encompassing segmentation, drawing, and assembling stages. Correct segmentation is crucial for generating accurate images. In the basic training phase, the segmentation network is trained using both labeled and unlabeled data, incorporating consistency learning to ensure consistent predictions before and after perturbations. The readjustment phase focuses on reducing segmentation errors by optimizing the segmentation network parameters based on the differences between redrawn and original CT images.We evaluated our method using two publicly available datasets: the beyond the cranial vault (BTCV) segmentation dataset (training: 44, validation: 6) and the abdominal multi‐organ segmentation (AMOS) challenge 2022 dataset (training:138, validation:16). Our results were compared with state‐of‐the‐art SSL methods, including MT and dual‐task consistency (DTC), using the Dice similarity coefficient (DSC) as an accuracy metric. On both datasets, our proposed SSL method consistently outperformed other methods, including supervised learning, achieving superior segmentation performance for various abdominal organs. These findings demonstrate the effectiveness of our approach, even with a limited number of labeled data.Our novel semi‐supervised learning approach for abdominal multi‐organ segmentation addresses the challenges associated with this task. By integrating a redrawing network and leveraging unlabeled data, we achieve remarkable improvements in accuracy. Our method demonstrates superior performance compared to existing SSL and supervised learning methods. This approach holds great promise in enhancing the precision and efficiency of multi‐organ segmentation in medical imaging applications. [ABSTRACT FROM AUTHOR]

The prediction of response to breast cancer therapy involves assessing the effectiveness of treatment by comparing biomarker levels before and after treatment. Deep learning (DL) models can provide a non-invasive and early way to evaluate the response to therapy based on medical imaging analysis. We conducted this systematic review to investigate the current DL based methods for predicting breast cancer therapy response using medical imaging. This review included 19 studies based on the PRISMA methodology. Some selected studies personalized the Convolutional Neural Network (CNN) architecture to improve its performance in handling medical images, while others used pre-trained models. The accuracy rates range from 0.73 to 0.90, and the Area Under the Curve (AUC) reaches 0.98. Our study's findings suggest that the performance of these approaches varies depending on various medical imaging modalities, the nature of the DL architecture used, and the fusion of training data sources. However, several challenges related to their explainability and generalizability arise. Therefore, it is necessary to develop larger datasets and broaden the scope of current studies to include multi-center studies. [ABSTRACT FROM AUTHOR]

Deep learning has been the subject of a significant amount of research interest in the development of novel algorithms for deep learning algorithms and medical image processing have proven very effective in a number of medical imaging tasks to help illness identification and diagnosis. The shortage of large-sized datasets that are also adequately annotated is a key barrier that is preventing the continued advancement of deep learning models used in medical image analysis, despite the effectiveness of these models. Over the course of the previous 5 years, a great number of research have concentrated on finding solutions to this problem. In this work, we present a complete overview of the use of deep learning techniques in a variety of medical image analysis tasks by reviewing and summarizing the current research that have been conducted in this area. In particular, we place an emphasis on the most recent developments and contributions of state-of-the-art semi-supervised and unsupervised deep learning in medical image analysis. These advancements and contributions are shortened based on various application scenarios, which include image registration, segmentation, classification and detection. In addition to this, we explore the significant technological obstacles that lie ahead and provide some potential answers for the ongoing study. [ABSTRACT FROM AUTHOR]

This systematic review aims to assess the efficacy of Artificial Intelligence (AI) applications in orthopedic surgery, with a focus on diagnostic accuracy and outcome prediction. In this review, we expose the findings of a systematic literature review awning the papers published from 2016 to October 2023 where authors worked on the application of an AI techniques and methods to an orthopedic purpose or problem. After application of inclusion and exclusion criteria on the extracted papers from PubMed and Google Scholar databases, 75 studies were included in this review. We examined, screened, and analyzed their content according to PRISMA guidelines. We also extracted data about the study design, the datasets included in the experiment, the reported performance measures and the results obtained. In this report, we will share the results of our survey by outlining the key machine and Deep Learning (DL) techniques, such as Convolutional Neural Network (CNN), Autoencoders and Generative Adversarial Network, that were mentioned, the various application domains in orthopedics, the type of source data and its modality, as well as the overall quality of their predictive capabilities. We aim to describe the content of the articles in detail and provide insights into the most notable trends and patterns observed in the survey data. [ABSTRACT FROM AUTHOR]

Abstract This paper introduces the efficient medical-images-aimed segment anything model (EMedSAM), addressing the high computational demands and limited adaptability of using SAM for medical image segmentation tasks. We present a novel, compact image encoder, DD-TinyViT, designed to enhance segmentation efficiency through an innovative parameter tuning method called med-adapter. The lightweight DD-TinyViT encoder is derived from the well-known ViT-H using a decoupled distillation approach.The segmentation and recognition capabilities of EMedSAM for specific structures are improved by med-adapter, which dynamically adjusts the model parameters specifically for medical imaging. We conducted extensive testing on EMedSAM using the public FLARE 2022 dataset and datasets from the First Hospital of Zhejiang University School of Medicine. The results demonstrate that our model outperforms existing state-of-the-art models in both multi-organ and lung segmentation tasks.

Abstract Traditionally, constructing training datasets for automatic muscle segmentation from medical images involved skilled operators, leading to high labor costs and limited scalability. To address this issue, we developed a tool that enables efficient annotation by non-experts and assessed its effectiveness for training an automatic segmentation network. Our system allows users to deform a template three-dimensional (3D) anatomical model to fit a target magnetic-resonance image using free-form deformation with independent control points for axial, sagittal, and coronal directions. This method simplifies the annotation process by allowing non-experts to intuitively adjust the model, enabling simultaneous annotation of all muscles in the template. We evaluated the quality of the tool-assisted segmentation performed by non-experts, which achieved a Dice coefficient greater than 0.75 compared to expert segmentation, without significant errors such as mislabeling adjacent muscles or omitting musculature. An automatic segmentation network trained with datasets created using this tool demonstrated performance comparable to or superior to that of networks trained with expert-generated datasets. This innovative tool significantly reduces the time and labor costs associated with dataset creation for automatic muscle segmentation, potentially revolutionizing medical image annotation and accelerating the development of deep learning-based segmentation networks in various clinical applications.

Abstract Computational intelligence techniques have emerged as a promising approach for diagnosing various medical conditions, including memory impairment. Increased abuse of psychoactive drugs poses a global public health burden, as repeated exposure to these substances can cause neurodegeneration, premature aging, and negatively affect memory impairment. Many studies in the literature relied on statistical studies, but they remained inaccurate. Some studies relied on physical data because the time factor was not considered, until Artificial Intelligence (AI) techniques came along that proved their worth in this diagnosis. The variable deep neural network method was used to adapt to the intermediate results and re-process the intermediate in case the result is undesirable. Computational intelligence was used in this study to classify a brain image from MRI or CT scans and to show the effectiveness of the dose ratio on health with treatment time, and to diagnose memory impairment in users of psychoactive substances. Understanding the neurotoxic profiles of psychoactive substances and the underlying pathways is hypothesized to be of great importance in improving the risk assessment and treatment of substance use disorders. The results proved the worth of the proposed method in terms of the accuracy of recognition rate as well as the possibility of diagnosis. It can be concluded that the diagnostic efficiency is increased by increasing the number of hidden layers in the neural network and controlling the weights and variables that control the deep learning algorithm. Thus, we conclude that good classification in this field may save human life or early detection of memory impairment.

Abstract Cervical cancer (CC) ranks as the fourth most common form of cancer affecting women, manifesting in the cervix. CC is caused by the Human papillomavirus (HPV) infection and is eradicated by vaccinating women from an early age. However, limited medical facilities present a significant challenge in mid- or low-income countries. It can improve the survivability rate and be successfully treated if the CC is detected at earlier stages. Current technological improvements allow for cost-effective, more sensitive, and rapid screening and treatment measures for CC. DL techniques are widely adopted for the automated detection of CC. DL techniques and architectures are used to detect CC and provide higher detection performance. This study offers the design of Enhanced Cervical Precancerous Lesions Detection and Classification using the Archimedes Optimization Algorithm with Transfer Learning (CPLDC-AOATL) algorithm. The CPLDC-AOATL algorithm aims to diagnose cervical cancer using medical images. At the preliminary stage, the CPLDC-AOATL technique involves a bilateral filtering (BF) technique to eliminate the noise in the input images. Besides, the CPLDC-AOATL technique applies the Inception-ResNetv2 model for the feature extraction process, and the use of AOA chose the hyperparameters. The CPLDC-AOATL technique involves a bidirectional long short-term memory (BiLSTM) model for the cancer detection process. The experimental outcome of the CPLDC-AOATL technique emphasized the superior accuracy outcome of 99.53% over other existing approaches under a benchmark dataset.

Introduction: Soft tissue sarcomas, similar in incidence to cervical and esophageal cancers, arise from various soft tissues like smooth muscle, fat, and fibrous tissue. Effective segmentation of sarcomas in imaging is crucial for accurate diagnosis. Methods: This study collected multi-modal MRI images from 45 patients with thigh soft tissue sarcoma, totaling 8,640 images. These images were annotated by clinicians to delineate the sarcoma regions, creating a comprehensive dataset. We developed a novel segmentation model based on the UNet framework, enhanced with residual networks and attention mechanisms for improved modality-specific information extraction. Additionally, self-supervised learning strategies were employed to optimize feature extraction capabilities of the encoders. Results: The new model demonstrated superior segmentation performance when using multi-modal MRI images compared to single-modal inputs. The effectiveness of the model in utilizing the created dataset was validated through various experimental setups, confirming the enhanced ability to characterize tumor regions across different modalities. Discussion: The integration of multi-modal MRI images and advanced machine learning techniques in our model significantly improves the segmentation of soft tissue sarcomas in thigh imaging. This advancement aids clinicians in better diagnosing and understanding the patient's condition, leveraging the strengths of different imaging modalities. Further studies could explore the application of these techniques to other types of soft tissue sarcomas and additional anatomical sites. [ABSTRACT FROM AUTHOR]

Traditionally, constructing training datasets for automatic muscle segmentation from medical images involved skilled operators, leading to high labor costs and limited scalability. To address this issue, we developed a tool that enables efficient annotation by non-experts and assessed its effectiveness for training an automatic segmentation network. Our system allows users to deform a template three-dimensional (3D) anatomical model to fit a target magnetic-resonance image using free-form deformation with independent control points for axial, sagittal, and coronal directions. This method simplifies the annotation process by allowing non-experts to intuitively adjust the model, enabling simultaneous annotation of all muscles in the template. We evaluated the quality of the tool-assisted segmentation performed by non-experts, which achieved a Dice coefficient greater than 0.75 compared to expert segmentation, without significant errors such as mislabeling adjacent muscles or omitting musculature. An automatic segmentation network trained with datasets created using this tool demonstrated performance comparable to or superior to that of networks trained with expert-generated datasets. This innovative tool significantly reduces the time and labor costs associated with dataset creation for automatic muscle segmentation, potentially revolutionizing medical image annotation and accelerating the development of deep learning-based segmentation networks in various clinical applications. [ABSTRACT FROM AUTHOR]

The classification of medical images enables physicians to perform expeditious and accurate data analysis, increasing the chances of timely disease diagnosis and early intervention to the patient. However, classification is a time‐consuming and labour intensive process when done manually. The Capsule Network (CapsNet) architecture has advantages in accurately and quickly classifying medical images due to its ability to evaluate images within part‐whole relationships, robustness to data rotations and affine transformations, and good performance on small datasets. However, CapsNet may demonstrate low performance on complex datasets. In this study, a new CapsNet model named MResCaps is proposed to overcome this disadvantage and enhance its performance on complex images. MResCaps utilizes an increasing number of residual blocks in each layer in parallel lane to obtain rich feature maps at different levels, aiming to achieve high success in the classification of various medical images. To evaluate the model's performance, the CIFAR10 dataset and the DermaMNIST, PneumoniaMNIST, and OrganMNIST‐S datasets from the MedMNIST dataset collection are used. MResCaps outperformed CapsNet by 20% in terms of accuracy on the CIFAR10 dataset. In addition, AUC values of 96.25%, 96.30%, and 97.12% were achieved in DermaMNIST, PneumoniaMNIST, and OrganMNIST‐S datasets, respectively. The results show that the proposed new model MResCaps improves the performance of CapsNet in the classification of complex and medical images. Furthermore, the model has demonstrated a better performance in comparison with extant studies in the literature. This study aims to contribute significantly to the literature by introducing a novel perspective on CapsNet‐based architectures for the classification of medical images through a parallel‐laned architecture and a rich feature capsule‐focused approach. [ABSTRACT FROM AUTHOR]

The application of magnetic resonance imaging (MRI) in the classification of brain tumors is constrained by the complex and time-consuming characteristics of traditional diagnostics procedures, mainly because of the need for a thorough assessment across several regions. Nevertheless, advancements in deep learning (DL) have facilitated the development of an automated system that improves the identification and assessment of medical images, effectively addressing these difficulties. Convolutional neural networks (CNNs) have emerged as steadfast tools for image classification and visual perception. This study introduces an innovative approach that combines CNNs with a hybrid attention mechanism to classify primary brain tumors, including glioma, meningioma, pituitary, and no-tumor cases. The proposed algorithm was rigorously tested with benchmark data from well-documented sources in the literature. It was evaluated alongside established pre-trained models such as Xception, ResNet50V2, Densenet201, ResNet101V2, and DenseNet169. The performance metrics of the proposed method were remarkable, demonstrating classification accuracy of 98.33%, precision and recall of 98.30%, and F1-score of 98.20%. The experimental finding highlights the superior performance of the new approach in identifying the most frequent types of brain tumors. Furthermore, the method shows excellent generalization capabilities, making it an invaluable tool for healthcare in diagnosing brain conditions accurately and efficiently. [ABSTRACT FROM AUTHOR]

In the field of dentistry, the presence of dental calculus is a commonly encountered issue. If not addressed promptly, it has the potential to lead to gum inflammation and eventual tooth loss. Bitewing (BW) images play a crucial role by providing a comprehensive visual representation of the tooth structure, allowing dentists to examine hard-to-reach areas with precision during clinical assessments. This visual aid significantly aids in the early detection of calculus, facilitating timely interventions and improving overall outcomes for patients. This study introduces a system designed for the detection of dental calculus in BW images, leveraging the power of YOLOv8 to identify individual teeth accurately. This system boasts an impressive precision rate of 97.48%, a recall (sensitivity) of 96.81%, and a specificity rate of 98.25%. Furthermore, this study introduces a novel approach to enhancing interdental edges through an advanced image-enhancement algorithm. This algorithm combines the use of a median filter and bilateral filter to refine the accuracy of convolutional neural networks in classifying dental calculus. Before image enhancement, the accuracy achieved using GoogLeNet stands at 75.00%, which significantly improves to 96.11% post-enhancement. These results hold the potential for streamlining dental consultations, enhancing the overall efficiency of dental services. [ABSTRACT FROM AUTHOR]

Computational intelligence techniques have emerged as a promising approach for diagnosing various medical conditions, including memory impairment. Increased abuse of psychoactive drugs poses a global public health burden, as repeated exposure to these substances can cause neurodegeneration, premature aging, and negatively affect memory impairment. Many studies in the literature relied on statistical studies, but they remained inaccurate. Some studies relied on physical data because the time factor was not considered, until Artificial Intelligence (AI) techniques came along that proved their worth in this diagnosis. The variable deep neural network method was used to adapt to the intermediate results and re-process the intermediate in case the result is undesirable. Computational intelligence was used in this study to classify a brain image from MRI or CT scans and to show the effectiveness of the dose ratio on health with treatment time, and to diagnose memory impairment in users of psychoactive substances. Understanding the neurotoxic profiles of psychoactive substances and the underlying pathways is hypothesized to be of great importance in improving the risk assessment and treatment of substance use disorders. The results proved the worth of the proposed method in terms of the accuracy of recognition rate as well as the possibility of diagnosis. It can be concluded that the diagnostic efficiency is increased by increasing the number of hidden layers in the neural network and controlling the weights and variables that control the deep learning algorithm. Thus, we conclude that good classification in this field may save human life or early detection of memory impairment. [ABSTRACT FROM AUTHOR]

According to the World Health Organization (WHO), melanoma is a type of cancer that affects people globally in different parts of the human body, leading to deaths of thousands of people every year worldwide. Intelligent diagnostic tools through automatic detection in medical images are extremely effective in aiding medical diagnosis. Computer-aided diagnosis (CAD) systems are of utmost importance for image-based pre-diagnosis, and the use of artificial intelligence-based tools for monitoring, detection, and segmentation of the pathological region are increasingly used in integrated smart solutions within smart city systems through cloud data processing with the use of edge computing. This study proposes a new approach capable of integrating into computational monitoring and medical diagnostic assistance systems called Health of Things Melanoma Detection System (HTMDS). The method presents a deep learning-based approach using the YOLOv8 network for melanoma detection in dermatoscopic images. The study proposes a workflow through communication between the mobile device, which extracts captured images from the dermatoscopic device and uploads them to the cloud API, and a new approach using deep learning and different fine-tuning models for melanoma detection and segmentation of the region of interest, along with the cloud communication structure and comparison with methods found in the state of the art, addressing local processing. The new approach achieved satisfactory results with over 98% accuracy for detection and over 99% accuracy for skin cancer segmentation, surpassing various state-of-the-art works in different methods, such as manual, semi-automatic, and automatic approaches. The new approach demonstrates effective results in the performance of different intelligent automatic models with real-time processing, which can be used in affiliated institutions or offices in smart cities for population use and medical diagnosis purposes. [ABSTRACT FROM AUTHOR]

Magnetic resonance imaging and computed tomography produce three-dimensional volumetric medical images. While a scalar value represents each individual volume element, or voxel, volumetric data are characterized by features derived from groups of neighboring voxels and their inherent relationships, which may vary depending on the specific clinical application. Labeled samples are also required in most applications, which can be problematic for large datasets such as medical images. We propose a direct volume rendering (DVR) framework based on multi-scale dimensionality reduction neighbor embedding that generates two-dimensional transfer function (TF) domains. In this way, we present FSS.t-SNE, a fast semi-supervised version of the t-distributed stochastic neighbor embedding (t-SNE) method that works over hundreds of thousands of voxels without the problem of crowding and with better separation in a 2D histogram compared to traditional TF domains. Our FSS.t-SNE scatters voxels of the same sub-volume in a wider region through multi-scale neighbor embedding, better preserving both local and global data structures and allowing for its internal exploration based on the original features of the multi-dimensional space, taking advantage of the partially provided labels. Furthermore, FSS.t-SNE untangles sample paths among sub-volumes, allowing us to explore edges and transitions. In addition, our approach employs a Barnes–Hut approximation to reduce computational complexity from O (N 2) (t-SNE) to O (N l o g N) . Although we require the additional step of generating the 2D TF domain from multiple features, our experiments show promising performance in volume segmentation and visual inspection. [ABSTRACT FROM AUTHOR]

Currently, brain tumors are extremely harmful and prevalent. Deep learning technologies, including CNNs, UNet, and Transformer, have been applied in brain tumor segmentation for many years and have achieved some success. However, traditional CNNs and UNet capture insufficient global information, and Transformer cannot provide sufficient local information. Fusing the global information from Transformer with the local information of convolutions is an important step toward improving brain tumor segmentation. We propose the Group Normalization Shuffle and Enhanced Channel Self-Attention Network (GETNet), a network combining the pure Transformer structure with convolution operations based on VT-UNet, which considers both global and local information. The network includes the proposed group normalization shuffle block (GNS) and enhanced channel self-attention block (ECSA). The GNS is used after the VT Encoder Block and before the downsampling block to improve information extraction. An ECSA module is added to the bottleneck layer to utilize the characteristics of the detailed features in the bottom layer effectively. We also conducted experiments on the BraTS2021 dataset to demonstrate the performance of our network. The Dice coefficient (Dice) score results show that the values for the regions of the whole tumor (WT), tumor core (TC), and enhancing tumor (ET) were 91.77, 86.03, and 83.64, respectively. The results show that the proposed model achieves state-of-the-art performance compared with more than eleven benchmarks. [ABSTRACT FROM AUTHOR]

Digital medical images play an increasingly important role in diagnosing and treating diseases in modern hospitals that interact with the Internet of Things environment. Some of these images are sensitive and confidential, especially when they involve a great deal of patient privacy. Maintaining the security of these medical images is challenging. Therefore, in this work, hybrid adaptive elliptic curve cryptography (AECC) and logistic map-based secure medical image transaction are proposed. Here, at first, we encrypt the image using the AECC technique. Then, to enhance the security of the image, again we encrypt the image using the Logistic Map-Based DNA Sequence encryption algorithm. The logistic map initial values are optimally selected using the Enhanced Mexican axolotl algorithm (EMA2). Finally, after decoding the diffused DNA matrix, we obtain the cipher image. The DNA encoding/decoding rules of the plain image and the key matrix are determined by the plain image. The performance of the proposed approach is analysed based on different metrics and efficiency compared with various algorithms. The experimental results show the proposed method attained the maximum security level of 96%, PSNR of 49.6, NPCR of 99.63%, and UACI of 33.77%. [ABSTRACT FROM AUTHOR]

The challenge of inaccurate information containment in synthetic images during the process of cross-domain medical image translation could be resolved by using a common strategy of integrating the loss of the feature consistency of the real/synthetic image as a penalty factor into the loss function of the translator. However, the existing methods are capable of using only the "domain-independent" feature of the image when the aligned images are scarcity, which results in the under-utilization of the image information. In the present study, a novel feature consistency loss computing and integration method based on the "domain-dependent" features was proposed, and a multi-category feature consistency–cross-domain image translation (MFC-CIT) model was constructed. The present study is the first to utilize the image feature information related to image domain in the process of cross-domain medical image translation. In the proposed method, the MFC module was first trained on the basis of supervised learning on a limited number of paired real images. Next, cross-domain image translation training based on unsupervised learning was performed on unpaired datasets by the CIT module, and this process was constrained by the loss of feature consistency of the real/synthetic image obtained in the MFC module. The experimental results on two datasets demonstrate that the proposed method effectively improves the translation accuracy of synthetic images. [ABSTRACT FROM AUTHOR]

Steganography is often used by hackers or illegal organizations as a vehicle for information interception of medical images. Exchanged between PACS or communicated during telemedicine sessions, images are modified to hide data. Such leaks through stego-images may result in the disclosure of doctors’ or patients’ data, or of sensitive hospital data posing thus major risks in terms of privacy and security of the information system. In this paper, to detect these illegal image-based communications, we propose a steganalysis approach, the originality of which relies on a novel neural network GTSCT-Net. This one first extracts texture features as global texture features based on the location specificity of different image parts and then extract possible steganographic information by composing multihead self-attention and deep convolution blocks. It also offers easier convergence and higher accuracy on a lower information embedding rate. Comparative experiments on private and public datasets show that the performance of GTSCT-Net for medical image intrusion detection is separately up to 10.12% and 2.97% better than recently advanced steganography detectors. [ABSTRACT FROM AUTHOR]

The automatic segmentation of cardiac computed tomography (CT) and magnetic resonance imaging (MRI) plays a pivotal role in the prevention and treatment of cardiovascular diseases. In this study, we propose an efficient network based on the multi-scale, multi-head self-attention (MSMHSA) mechanism. The incorporation of this mechanism enables us to achieve larger receptive fields, facilitating the accurate segmentation of whole heart structures in both CT and MRI images. Within this network, features extracted from the shallow feature extraction network undergo a MHSA mechanism that closely aligns with human vision, resulting in the extraction of contextual semantic information more comprehensively and accurately. To improve the precision of cardiac substructure segmentation across varying sizes, our proposed method introduces three MHSA networks at distinct scales. This approach allows for fine-tuning the accuracy of micro-object segmentation by adapting the size of the segmented images. The efficacy of our method is rigorously validated on the Multi-Modality Whole Heart Segmentation (MM-WHS) Challenge 2017 dataset, demonstrating competitive results and the accurate segmentation of seven cardiac substructures in both cardiac CT and MRI images. Through comparative experiments with advanced transformer-based models, our study provides compelling evidence that despite the remarkable achievements of transformer-based models, the fusion of CNN models and self-attention remains a simple yet highly effective approach for dual-modality whole heart segmentation. [ABSTRACT FROM AUTHOR]

Cervical cancer (CC) ranks as the fourth most common form of cancer affecting women, manifesting in the cervix. CC is caused by the Human papillomavirus (HPV) infection and is eradicated by vaccinating women from an early age. However, limited medical facilities present a significant challenge in mid- or low-income countries. It can improve the survivability rate and be successfully treated if the CC is detected at earlier stages. Current technological improvements allow for cost-effective, more sensitive, and rapid screening and treatment measures for CC. DL techniques are widely adopted for the automated detection of CC. DL techniques and architectures are used to detect CC and provide higher detection performance. This study offers the design of Enhanced Cervical Precancerous Lesions Detection and Classification using the Archimedes Optimization Algorithm with Transfer Learning (CPLDC-AOATL) algorithm. The CPLDC-AOATL algorithm aims to diagnose cervical cancer using medical images. At the preliminary stage, the CPLDC-AOATL technique involves a bilateral filtering (BF) technique to eliminate the noise in the input images. Besides, the CPLDC-AOATL technique applies the Inception-ResNetv2 model for the feature extraction process, and the use of AOA chose the hyperparameters. The CPLDC-AOATL technique involves a bidirectional long short-term memory (BiLSTM) model for the cancer detection process. The experimental outcome of the CPLDC-AOATL technique emphasized the superior accuracy outcome of 99.53% over other existing approaches under a benchmark dataset. [ABSTRACT FROM AUTHOR]

Medical image processing has been used in medical image analysis for many years and has achieved great success. However, one challenge is that medical image processing algorithms ineffectively utilize multi-modality characteristics to further extract features. To address this issue, we propose SSGNet based on UNet, which comprises a selective multi-scale receptive field (SMRF) module, a selective kernel self-attention (SKSA) module, and a skip connection attention module (SCAM). The SMRF and SKSA modules have the same function but work in different modality groups. SMRF functions in the T1 and T1ce modality groups, while SKSA is implemented in the T2 and FLAIR modality groups. Their main tasks are to reduce the image size by half, further extract fused features within the groups, and prevent information loss during downsampling. The SCAM uses high-level features to guide the selection of low-level features in skip connections. To improve performance, SSGNet also utilizes deep supervision. Multiple experiments were conducted to evaluate the effectiveness of our model on the BraTS2018 dataset. SSGNet achieved Dice coefficient scores for the whole tumor (WT), tumor core (TC), and enhancing tumor (ET) of 91.04, 86.64, and 81.11, respectively. The results show that the proposed model achieved state-of-the-art performance compared with more than twelve benchmarks. [ABSTRACT FROM AUTHOR]

Efficient magnetic resonance imaging (MRI) segmentation, which is helpful for treatment planning, is essential for identifying brain tumors from detailed images. In recent years, various convolutional neural network (CNN) structures have been introduced for brain tumor segmentation tasks and have performed well. However, the downsampling blocks of most existing methods are typically used only for processing the variation in image sizes and lack sufficient capacity for further extraction features. We, therefore, propose SARFNet, a method based on UNet architecture, which consists of the proposed SLiRF module and advanced AAM module. The SLiRF downsampling module can extract feature information and prevent the loss of important information while reducing the image size. The AAM block, incorporated into the bottleneck layer, captures more contextual information. The Channel Attention Module (CAM) is introduced into skip connections to enhance the connections between channel features to improve accuracy and produce better feature expression. Ultimately, deep supervision is utilized in the decoder layer to avoid vanishing gradients and generate better feature representations. Many experiments were performed to validate the effectiveness of our model on the BraTS2018 dataset. SARFNet achieved Dice coefficient scores of 90.40, 85.54, and 82.15 for the whole tumor (WT), tumor core (TC), and enhancing tumor (ET), respectively. The results show that the proposed model achieves state-of-the-art performance compared with twelve or more benchmarks. [ABSTRACT FROM AUTHOR]

The use of artificial intelligence within the healthcare sector is consistently growing. However, the majority of deep learning-based AI systems are of a black box nature, causing these systems to suffer from a lack of transparency and credibility. Due to the widespread adoption of medical imaging for diagnostic purposes, the healthcare industry frequently relies on methods that provide visual explanations, enhancing interpretability. Existing research has summarized and explored the usage of visual explanation methods in the healthcare domain, providing introductions to the methods that have been employed. However, existing reviews are frequently used for interpretable analysis in the medical field ignoring comprehensive reviews on Class Activation Mapping (CAM) methods because researchers typically categorize CAM under the broader umbrella of visual explanations without delving into specific applications in the healthcare sector. Therefore, this study primarily aims to analyze the specific applications of CAM-based deep explainable methods in the healthcare industry, following the PICO (Population, Intervention, Comparison, Outcome) framework. Specifically, we selected 45 articles for systematic review and comparative analysis from three databases—PubMed, Science Direct, and Web of Science—and then compared eight advanced CAM-based methods using five datasets to assist in method selection. Finally, we summarized current hotspots and future challenges in the application of CAM in the healthcare field. [ABSTRACT FROM AUTHOR]

Summary: In the process of modern medical diagnosis, medical image‐assisted diagnosis plays a very important role. However, the process of medical image acquisition, will be affected by various types and degrees of noise, and there will be a certain probability of producing strip artifacts, which will interfere with the doctor's diagnosis, analysis, and treatment of diseases to a certain extent. However, the traditional medical image denoising method will cause problems such as image edge blurring and detail loss, and it is difficult to achieve the balance between noise removal and detail information retention. Therefore, denoising medical images and improving the accuracy of denoising as much as possible have very important scientific research significance and clinical application value. Based on this, this article proposes a medical image denoising method based on a double residual convolutional neural network and compares it with traditional medical images denoising methods such as K‐SVD, BM3D, and PNLM3. Experimental results show that the medical image denoising method based on the double residual convolutional neural network proposed in this article has excellent performance. [ABSTRACT FROM AUTHOR]

This study investigated the influence of using three-dimensional (3D) computer and 3D-printed models on the spatial reasoning of experts and novices. The task of this study required general university students as novices in Experiment 1 and surgeons specializing in digestive surgery as experts in Experiment 2 to infer the cross sections of a liver, using a 3D-computer or 3D-printed model. The results of the experiments showed that the university students learned faster and inferred the liver structure more accurately with the 3D-printed model than with the 3D-computer model. Conversely, the surgeons showed the same task performance when using the 3D-computer and 3D-printed models; however, they performed the task with more confidence and less workload during the task with the 3D-printed model. Based on the results, the cognitive effects and advantages of using 3D-printed models for novices and experts have been discussed. [ABSTRACT FROM AUTHOR]

Type II fuzzy sets consider the uncertainties involved in the membership function of classical fuzzy set theory. The membership function of a Type II fuzzy set is obtained by blurring the boundaries of the original fuzzy set membership function. The interval-based modified Type II fuzzy set method is presented in this paper to measure the fuzziness present in medical images. Using Hamacher T-conorm as the aggregation operator, the membership functions of the upper and lower intervals have been combined to obtain the contrast-enhanced image. For experimental analysis, quantitative and qualitative metrics have been evaluated for different kinds of medical data sets. To test the efficiency of the proposed technique, the computed results are compared with state-of-the-art techniques. The qualitative and quantitative results clearly demonstrate that the performance of the proposed techniques is much better than the existing techniques for almost all the image data sets. The results evaluated for average values with standard deviation for all the datasets bear witness to the performance of the proposed technique. The mean opinion score and the processing time also support the efficacy of the proposed technique, which is much better than most state-of-the-art techniques except at some of the cases. [ABSTRACT FROM AUTHOR]

BACKGROUND: Rapid and accurate segmentation of tumor regions from rectal cancer images can better understand the patient’s lesions and surrounding tissues, providing more effective auxiliary diagnostic information. However, cutting rectal tumors with deep learning still cannot be compared with manual segmentation, and a major obstacle to cutting rectal tumors with deep learning is the lack of high-quality data sets. OBJECTIVE: We propose to use our Re-segmentation Method to manually correct the model segmentation area and put it into training and training ideas. The data set has been made publicly available. Methods: A total of 354 rectal cancer CT images and 308 rectal region images labeled by experts from Jiangxi Cancer Hospital were included in the data set. Six network architectures are used to train the data set, and the region predicted by the model is manually revised and then put into training to improve the ability of model segmentation and then perform performance measurement. RESULTS: In this study, we use the Resegmentation Method for various popular network architectures. CONCLUSION: By comparing the evaluation indicators before and after using the Re-segmentation Method, we prove that our proposed Re-segmentation Method can further improve the performance of the rectal cancer image segmentation model. [ABSTRACT FROM AUTHOR]

This article proposes a target image processing method based on super-resolution reconstruction and machine learning algorithms, which solves the low-resolution problem in medical images during imaging. This method uses nonlocal autoregressive learning based on a medical image super-resolution reconstruction method. The autoregressive model is introduced into the sparse representation-based medical image super-resolution reconstruction model by utilizing medical image data inherent nonlocal similarity characteristics. At the same time, a clustering algorithm is used to obtain a classification dictionary, improving experimental efficiency. The experimental results show that ten randomly selected CT/MR images are used as test images, and each image's peak signal-to-noise ratio and structural similarity values are calculated separately. Compared with other methods, the method proposed in this paper is significantly better and can achieve ideal results, with the highest value being 31.49. This method demonstrates the feasibility of using super-resolution reconstruction and machine learning algorithms in medical image resolution. [ABSTRACT FROM AUTHOR]

One of the most important areas in medical image analysis is segmentation, in which raw image data is partitioned into structured and meaningful regions to gain further insights. By using Deep Neural Networks (DNN), AI-based automated segmentation algorithms can potentially assist physicians with more effective imaging-based diagnoses. However, since it is difficult to acquire high-quality ground truths for medical images and DNN hyperparameters require significant manual tuning, the results by DNN-based medical models might be limited. A potential solution is to combine multiple DNN models using ensemble learning. We propose a two-layer ensemble of deep learning models in which the prediction of each training image pixel made by each model in the first layer is used as the augmented data of the training image for the second layer of the ensemble. The prediction of the second layer is then combined by using a weight-based scheme which is found by solving linear regression problems. To the best of our knowledge, our paper is the first work which proposes a two-layer ensemble of deep learning models with an augmented data technique in medical image segmentation. Experiments conducted on five different medical image datasets for diverse segmentation tasks show that proposed method achieves better results in terms of several performance metrics compared to some well-known benchmark algorithms. Our proposed two-layer ensemble of deep learning models for segmentation of medical images shows effectiveness compared to several benchmark algorithms. The research can be expanded in several directions like image classification. [ABSTRACT FROM AUTHOR]

Maintaining the secrecy of sensitive data transferred over the Internet while restricting access to approved information is a significant security and protection challenge in telemedicine today. In this work, we present a trustworthy and blind watermarking approach for medical photographs using the Integer Wavelet Transform (IWT) and Singular Value Decomposition (SVD) to preserve the privacy of such information. The embedding capacity of contemporary integer wavelet transform (IWT) based watermarking systems may be constrained. To overcome this specific issue, the study offers an IWT-based secure big capacity watermarking method. According to experiment results on imperceptibility and resilience, the suggested technique efficiently preserves a significant amount of quality in watermarked images, and the watermark is resistant to the most common attacks in watermarking. [ABSTRACT FROM AUTHOR]