AUTOMATED MEDICAL IMAGE ANALYSIS FOR DISEASE DETECTION AND DIAGNOSIS

Abstract

Computerized medical image analysis has become a revolutionary technology in contemporary healthcare, making it possible to detect and diagnose diseases at a faster pace, with greater precision, and lower cost. Using cutting-edge methods like machine learning, deep learning, and computer vision, medical imaging systems can interpret sophisticated medical images like X-rays, CT scans, MRIs, and ultrasounds automatically. These systems support clinicians by discovering patterns, outliers, and early markers of diseases like cancer, neurological disorders, cardiovascular ailments, and more. This automation not only increases diagnostic accuracy but also lightens the load of healthcare workers and minimizes the scope for human error. The integration of artificial intelligence in medical imaging opens doors to customized treatment plans and better patient outcomes. This article examines the approaches, resources, and clinical use of computerized medical image analysis, as well as the challenges of data quality, interpretability, and ethical considerations. Recent developments in convolutional neural networks (CNNs), segmentation algorithms, and image classification methods have greatly enhanced the accuracy and consistency of computerized diagnostic systems. Furthermore, coupling with electronic health records (EHRs) and real-time data processing enables a comprehensive understanding of a patient's health status.