The accurate analysis of medical images is vital for diagnosing and predicting medical
conditions. Traditional approaches relying on radiologists and clinicians suffer from …

Abstract Convolutional Neural Networks (CNNs) have advanced existing medical systems
for automatic disease diagnosis. However, there are still concerns about the reliability of …

Abstract Convolutional Neural Networks (CNNs) have advanced existing medical systems
for automatic disease diagnosis. However, there are still concerns about the reliability of …

Medical image analysis is a hot research topic because of its usefulness in different clinical
applications, such as early disease diagnosis and treatment. Convolutional neural networks …

Convolutional Neural Networks (CNNs) have reigned for a decade as the de facto approach
to automated medical image diagnosis, pushing the state-of-the-art in classification …

This paper introduces Knowledge Distillation of Vision Transformer (KDViT), a novel
approach for medical image classification. The Vision Transformer architecture incorporates …

Convolutional Neural Networks (CNNs) have reigned for a decade as the de facto approach
to automated medical image diagnosis, pushing the state-of-the-art in classification …

The COVID-19 global health crisis has presented daunting challenges to medical
professionals, making accurate and efficient diagnoses more important than ever. In view of …

Medical imaging is an integral part of disease diagnosis and treatment. However,
interpreting medical images can be time-consuming and subjective, making it challenging …

In recent years, convolutional neural networks have shown significant success and are
frequently used in medical image analysis applications. However, the convolution process in …