Deep learning in medical imaging for disease diagnosis

Deep learning plays a significant role in transforming medical imaging for disease diagnosis. It uses advanced algorithms, especially Convolutional Neural Networks (CNNs), to automatically learn and extract important features from medical images. This technology helps in detecting, classifying, and diagnosing various diseases, such as different types of cancer, brain disorders like aneurysms and strokes, heart diseases, and respiratory conditions. Deep learning improves the accuracy and efficiency of diagnostic workflows and reduces the workload for healthcare professionals. Despite its many advantages, deep learning faces challenges related to data availability, model interpretability, and clinical validation. This review highlights the current applications, performance evaluation methods, and challenges of deep learning in medical imaging for disease diagnosis.