The rapid advancement of medical imaging technologies requires the development of advanced, automated, and interpretable diagnostic tools for clinical decision-making. Although convolutional neural networks (CNNs) have shown significant promise in medical image analysis, they have limitations in capturing the global context and lack interpretability, thereby hindering their clinical adoption. This study presents EFFResNet-ViT, a novel hybrid deep learning (DL) model designed to address these challenges by combining EfficientNet-B0 and ResNet-50 CNN backbones with a vision transformer (ViT) module. The proposed architecture employs a feature fusion strategy to integrate the local feature extraction strengths of CNNs with the global dependency modeling capabilities of transformers. The extracted features are further refined through a post-transformer CNN and a global average pooling layer to enhance the classification performance. To improve interpretability, EFFResNet-ViT incorporates Grad-CAM visualization techniques to highlight regions contributing to classification decisions and employs t-distributed stochastic neighbor embedding for feature space analysis, providing insights into class separability. The proposed model was evaluated on two benchmark datasets: brain tumor (BT) CE-MRI for brain tumor classification and a retinal image dataset for ophthalmological diagnosis. EFFResNet-ViT achieved state-of-the-art performance, with accuracies of 99.31% and 92.54% on the BT CE-MRI and retinal datasets, respectively. Comparative analyses demonstrate the superior classification performance and interpretability of EFFResNet-ViT over existingViT and CNN-based hybrid models. The explainable design of EFFResNet-ViT addresses the critical need for transparency in artificial intelligence-driven medical diagnostics, facilitating its potential integration into clinical workflows to improve decision-making and patient outcomes.

We present an analysis and evaluation of breast cancer detection and diagnosis using segmentation models. We used an advanced semantic segmentation method and a deep convolutional neural network to identify the Breast Imaging Reporting and Data System (BI-RADS) lexicon for breast ultrasound images. To improve the segmentation results, we used six models to analyse 309 patients, including 151 benign and 158 malignant tumour images. We compared the Unet3+ architecture with several other models, such as FCN, Unet, SegNet, DeeplabV3+ and pspNet. The Unet3+ model is a state-of-the-art, semantic segmentation architecture that showed optimal performance with an average accuracy of 82.53% and an average intersection over union (IU) of 52.57%. The weighted IU was found to be 89.14% with a global accuracy of 90.99%. The application of these types of segmentation models to the detection and diagnosis of breast cancer provides remarkable results. Our proposed method has the potential to provide a more accurate and objective diagnosis of breast cancer, leading to improved patient outcomes.

Introduction: In this study, we harnessed three cutting-edge algorithms' capabilities to refine the elbow fracture prediction process through X-ray image analysis. Employing the YOLOv8 (You only look once) algorithm, we first identified Regions of Interest (ROI) within the X-ray images, significantly augmenting fracture prediction accuracy. Methods: Subsequently, we integrated and compared the ResNet, the SeResNet (Squeeze-and-Excitation Residual Network) ViT (Vision Transformer) algorithms to refine our predictive capabilities. Furthermore, to ensure optimal precision, we implemented a series of meticulous refinements. This included recalibrating ROI regions to enable finer-grained identification of diagnostically significant areas within the X-ray images. Additionally, advanced image enhancement techniques were applied to optimize the X-ray images' visual quality and structural clarity. Results: These methodological enhancements synergistically contributed to a substantial improvement in the overall accuracy of our fracture predictions. The dataset utilized for training, testing & validation, and comprehensive evaluation exclusively comprised elbow X-ray images, where predicting the fracture with three algorithms: Resnet50; accuracy 0.97, precision 1, recall 0.95, SeResnet50; accuracy 0.97, precision 1, recall 0.95 & ViTB- 16 with high accuracy of 0.99, precision same as the other two algorithms, with a recall of 0.95. Conclusion: This approach has the potential to increase the precision of diagnoses, lessen the burden of radiologists, easily integrate into Current Medical Imaging Formerly Current Medical Imaging Reviews systems, and assist clinical decision-making, all of which could lead to better patient care and health outcomes overall.

Breast cancer is still one of the most common and deadly types of cancer that affect women worldwide, hence improvements in diagnostic techniques are required for early identification and treatment. This review paper discusses the latest developments in the identification and analysis of breast cancers in medical imaging, as well as the comparative effectiveness of different techniques for segmentation and machine learning algorithms. We systematically analyze research that use advanced machine learning techniques including convolutional neural networks (CNNs), support vector machines (SVMs), and deep learning models in addition to more conventional segmentation techniques like thresholding, region-based, and edge detection methods. In comparison to traditional segmentation techniques, the synthesis of findings from the reviewed literature highlights the superiority of machine learning algorithms, especially deep learning techniques, in obtaining improved accuracy, sensitivity, and specificity in breast tumor detection. Moreover, combining segmentation methods with sophisticated machine learning models appears to be a viable way to improve tumor delineation and classification efficiency. In order to improve breast cancer diagnosis and treatment, this study intends to give researchers and medical professionals a thorough assessment of the advantages, disadvantages, and potential future directions of using these technologies.

The use of artificial intelligence (AI) into medical devices and remote healthcare has led to a rapid advancement in modern medicine. Unprecedented capabilities in patient monitoring, diagnosis, and treatment delivery have been made possible by this combination. The state of AI applications in medical devices and remote healthcare is thoroughly investigated in this review paper, which also explores at the prospects, challenges and problems facing the healthcare sector. As healthcare systems worldwide deal with rising expectations for accuracy and efficiency, artificial intelligence's potential to enhance medical devices and remote healthcare services is becoming increasingly significant. This paper examines the possible future effects on healthcare of major developments in wearable technology, telemedicine platforms, and AI-powered diagnostic tools.