CNN-Based Medical Image Classification: A Deep Learning Framework for Automated Clinical Diagnosis

Today, medical image classification is a vital part of modern diagnostics, helping doctors identify diseases with much greater speed and accuracy. Older methods were often held back because they required experts to manually define features, which made them difficult to scale and less reliable. That’s where Convolutional Neural Networks (CNNs) come in. As a type of deep learning, they perform exceptionally well because they teach themselves to recognize complex patterns directly from raw image data, rather than needing manual guidance. In this study, we introduce a complete CNN-based framework designed to tackle major roadblocks like having limited labelled data, dealing with class imbalances, and overcoming the problem where AI models are difficult to interpret. We used a transfer learning approach and tested it against a variety of benchmarks, including brain MRIs, chest X-rays, and dermoscopic images. When we compared our framework to both traditional machine learning techniques and standard deep learning architectures, the results were clear. Our CNN models significantly outperformed the existing methods, consistently achieving over 90% accuracy while remaining stable across different types of datasets.