IMAGE RECOGNITION FOR MEDICAL DIAGNOSIS

Abstract

Medical diagnosis using imaging plays a vital role in early disease detection and treatment planning. Traditional diagnosis relies heavily on manual interpretation by medical experts, which can be time consuming and prone to human error. This project presents an Image Recognition System for Medical Diagnosis using machine learning and deep learning techniques. Medical images such as X-rays, MRI, CT scans, and ultrasound images are analyzed automatically. Image recognition helps identify patterns and abnormalities that indicate diseases. Convolutional Neural Networks (CNNs) are used for feature extraction and classification. The system improves diagnostic accuracy and consistency. Automated analysis reduces workload on doctors. Early detection improves patient outcomes. The proposed system supports faster and reliable medical diagnosis using artificial intelligence.