AI Technology in Medical Image Analysis

This Reprint presents eleven original research papers pushing the boundaries of automated medical decision support across diverse imaging modalities, including X-rays, MRI, CT, and histopathological scans. The presented works address critical diagnostic and therapeutic challenges using state-of-the-art artificial intelligence frameworks, such as Deep Convolutional Neural Networks (DCNNs), ResNet-50, LSTM architectures, and innovative semi-supervised learning models. Key breakthroughs presented in this volume include high-accuracy multi-class breast cancer classification, age estimation from brain scans, advanced automated quality assurance systems for chest X-rays, and customized algorithmic recommendations for orthopedic care. Furthermore, contributors explore groundbreaking methods in multi-modality thalamus segmentation, hyperparameter weight-space optimization, data augmentation techniques for data-scarce domains like gastric cancer, and high-precision lung disease detection. By prioritizing clinical efficiency, diagnostic speed, and data conformity, these studies collectively offer powerful solutions to streamline workflows, mitigate radiologist burnout, and reduce healthcare resource costs. This Reprint underscores the transformative role of AI as an essential ally to clinicians, advancing toward highly accurate, early disease detection and personalized patient care worldwide.