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DIAGNOSIS PREDICTION IN MEDICAL IMAGING: A COMPARATIVE ANALYSIS OF CLASSIFIERS
Medical imaging is vital for diagnosing diseases and guiding treatment across specialties like radiology, oncology, cardiology, and neurology. Advances in machine learning (ML) and artificial intelligence offer opportunities to enhance diagnosis prediction from imaging data (e.g., X-rays, MRIs, CT scans, ultrasounds). This project focuses on a comparative analysis of ML classifiers for accurate diagnosis prediction, aiming to improve efficiency, consistency, and patient outcomes in healthcare. Traditional diagnosis relies on radiologists’ expertise, which, while effective, can be timeconsuming, subjective, and limited in leveraging all image information. This project addresses challenges like noise, variable image quality, class imbalance, and robust feature extraction by developing and evaluating ML classifiers, such as Logistic Regression and Extra Trees. Feature engineering will extract relevant image data, while model optimization and ensemble methods will enhance performance. The comparative analysis will use metrics like accuracy, sensitivity, and specificity to identify the most effective classifier. The motivation is to augment medical professionals’ capabilities with quantitative, ML-driven insights, enabling faster and more accurate diagnoses. This can optimize resource allocation and improve patient outcomes. By systematically evaluating classifiers and addressing imaging challenges, this project aims to advance diagnostic tools, making them more reliable and scalable across medical specialties. The findings could transform healthcare by integrating AI into routine diagnostic workflows, ultimately supporting clinicians in delivering precise, timely, and data-driven care.
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