Computer Aided

Computer-aided diagnosis (CAD) leverages machine learning, particularly deep neural networks like convolutional neural networks (CNNs) and variations such as DenseNet and ResNet, to assist in medical image analysis and disease diagnosis. Current research focuses on improving diagnostic accuracy across various medical imaging modalities (X-ray, CT, MRI) and addressing challenges like limited data availability through techniques such as self-supervised learning, generative active learning, and contrastive learning guided by radiologist gaze patterns. These advancements hold significant promise for improving diagnostic efficiency, accuracy, and accessibility, particularly in resource-constrained settings and for diseases with overlapping symptoms.