Medical Image Classification

Medical image classification uses machine learning to automatically categorize medical images (e.g., X-rays, MRIs) for diagnosis and treatment planning. Current research emphasizes improving model generalizability across diverse datasets and handling challenges like class imbalance and noisy labels, often employing convolutional neural networks (CNNs), vision transformers (ViTs), and foundation models adapted for medical data. These advancements aim to enhance diagnostic accuracy, efficiency, and accessibility, particularly in resource-constrained settings, while also addressing issues of model interpretability and fairness.