Machine Learning for Image Processing in Healthcare
Mohamed Adel Hammad, Sadique Ahmad · Advances in computational intelligence and robotics book series · 2025
This chapter provides a comprehensive exploration of machine learning applications in medical image analysis across various imaging modalities including X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and positron emission tomography (PET). We begin by examining the fundamental principles of medical imaging and traditional image processing techniques before delving into machine learning paradigms—supervised, unsupervised, and semi-supervised learning approaches—that have been successfully applied to healthcare imaging challenges. The evolution from conventional machine learning algorithms to advanced deep learning architectures is thoroughly discussed, with particular emphasis on convolutional neural networks (CNNs) and their specialized variants such as U-Net for medical image segmentation. The chapter presents detailed applications across clinical domains, including disease detection, organ segmentation, computer-aided diagnosis, and treatment response prediction.