IMAGE PROCESSING TECHNIQUES FOR AUTOMATED MEDICAL DIAGNOSIS

Abstract

Automated medical diagnosis using image processing has emerged as a transformative approach in modern healthcare systems. Medical images such as X-rays, MRI, CT scans, ultrasound, and histopathological images provide critical information for disease detection and monitoring. However, manual interpretation of these images is timeconsuming and prone to inter-observer variability. Image processing techniques combined with machine learning and deep learning methods enable accurate, fast, and consistent diagnostic decisions. This paper presents a comprehensive study of image processing techniques applied to automated medical diagnosis. It discusses preprocessing, segmentation, feature extraction, and classification stages involved in medical image analysis. The proposed methodology integrates advanced filtering, feature learning, and classification models to improve diagnostic accuracy. Experimental evaluation demonstrates the effectiveness of the approach across multiple medical imaging datasets. The results highlight improved performance compared to traditional diagnostic methods. The study emphasizes the role of intelligent image analysis in enhancing clinical decision support systems.