An Intelligent Multi-Class Medical Image Analysis Framework for Disease Diagnosis

Abstract

A sophisticated use of artificial intelligence, multi-class medical image analysis for illness diagnosis helps with the automatic identification and categorization of several diseases from medical photographs. Accurate and effective diagnostic systems are becoming more and more necessary as medical imaging technologies like MRIs, CT scans, and X-rays expand quickly. Conventional manual diagnosis is prone to human error and can be time-consuming. Deep learning methods, in particular Convolutional Neural Networks (CNNs), are frequently employed to evaluate intricate visual patterns and categorize them into various illness groups in order to overcome this difficulty.The objectives of this system are to facilitate early disease identification, lessen the workload for medical professionals, and increase diagnostic accuracy. The system learns to distinguish between different illness conditions like pneumonia, TB, cancer, and normal instances by using vast datasets of annotated medical images to train models. The suggested method aids physicians with accurate forecasts and improves clinical decision-making. All things considered, multi-class medical image analysis is important to contemporary healthcare since it offers quicker, more affordable, and precise illness diagnosis.