Application of Artificial Intelligence in Medical Image Analysis for Disease Detection Using Convolutional Neural Networks

Abstract

The rapid development of Artificial Intelligence (AI) has significantly transformed various domains, particularly the healthcare sector. One of the most prominent applications of AI is in medical image analysis, where automated systems are developed to assist clinicians in diagnosing diseases more accurately and efficiently. This study explores the implementation of Convolutional Neural Networks (CNN) for disease detection using medical imaging data. CNN models have the capability to automatically extract hierarchical features from raw image inputs, eliminating the need for manual feature engineering. In this research, a CNN-based framework is designed, trained, and evaluated using standard performance metrics including accuracy, precision, recall, and F1-score. The findings demonstrate that CNN-based approaches achieve high performance and show strong potential in supporting clinical decision-making processes. However, challenges such as limited annotated datasets, model interpretability, and generalization remain critical issues that need to be addressed in future research.