Federated Learning for Medical Image Analysis Using Convolutional Neural Networks

Abstract

Medical image segmentation is an important part of healthcare since it lets doctors clearly see anatomical features and diseased areas for diagnosis, therapy planning, and monitoring. CNN works fine with different types of medical images like MRI, CT and ultrasound for segmentizing it precisely. In this chapter all available CNN based medical image segmentation techniques are discussed. Many issues are addressed here like imbalance in class and unavailability of annotated data. Important architectures like U-Net and DeepLab are discussed in detail based on their segmentation process. Detail discussions are made about datasets, preprocessing methods, training mechanism, and optimization techniques that are specific to medical imaging. Case studies show that CNNs gets Dice coefficients higher than 0.9 on benchmark datasets like BraTS and ISIC. Future directions, encompassing integration with transformers and explainable AI, are examined to facilitate clinical adoption. This chapter offers an extensive examination of the transformative influence of CNNs on medical picture segmentation and delineates potential directions for future research.