Title: Artificial Intelligence: Theory, Tools and Real-World Applications
Editors: Prof. Dr. A. Shameem and Dr. J. Rengamani
ISBN: 978-81-69857-67-3
Chapter: 15
DOI: https://doi.org/10.59646/819/21
Author: Ms. B. Geno Cinthia
Abstract
Deep learning has emerged as a transformative technology in the field of medical image analysis, enabling healthcare professionals to improve diagnostic accuracy, efficiency, and patient outcomes. By utilizing advanced neural network architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and transformer-based models, deep learning systems can automatically extract meaningful features from complex medical images, including X-rays, Magnetic Resonance Imaging (MRI), Computed Tomography (CT) scans, ultrasound images, and histopathological slides. These technologies assist in the early detection and classification of diseases such as cancer, cardiovascular disorders, neurological conditions, and respiratory illnesses. Deep learning models have demonstrated remarkable performance in image segmentation, anomaly detection, disease prediction, and treatment planning, often achieving accuracy comparable to or exceeding that of human experts. Furthermore, the integration of artificial intelligence into medical imaging workflows reduces diagnostic time, minimizes human error, and supports personalized healthcare decisions. Despite challenges related to data privacy, model interpretability, and the need for large annotated datasets, ongoing advancements in explainable AI and federated learning continue to enhance the reliability and adoption of deep learning in healthcare. This study explores the practical applications, benefits, challenges, and future prospects of deep learning technologies in medical image analysis, highlighting their significant role in modern medical practice and digital healthcare transformation.
Keywords: Deep Learning, Medical Image Analysis, Artificial Intelligence, Convolutional Neural Networks, Medical Imaging, Disease Detection, Image Segmentation, Computer-Aided Diagnosis, Healthcare Technology, Diagnostic Accuracy, MRI Analysis, CT Scan Interpretation, Explainable AI, Precision Medicine, Digital Healthcare.