CNN in Medical Imaging
Affaan Shaikh, Ravi Teja Kothuru · Advances in computational intelligence and robotics book series · 2025
Convolutional Neural Networks (CNNs) have revolutionized medical imaging, enhancing the accuracy, efficiency, and depth of analysis for complex data such as X-rays, MRIs, and CT scans. Their ability to detect subtle patterns offers significant advantages in early diagnosis, prognosis, and disease monitoring, particularly for conditions like cancer, neurological disorders, and cardiovascular diseases. Despite these benefits, CNN applications face challenges, including the need for large and diverse datasets, high computational demands, and limited interpretability, which can impact clinical decision-making. This chapter explores the advantages of CNNs in medical imaging, highlights key challenges, and discusses future opportunities for real-time analysis, multimodal integration, and personalized medicine. This chapter also goes through the types of CNNs and their individual applications and real time insights. It also explores different types and architectures of CNNs, highlighting their unique applications and potential for providing real-time insights.