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Applications, Challenges, and Future Prospects of Enhancing Medical Imaging With Neural Networks
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Author(s): Aman Dubey (Maulana Azad National Institute of Technology, India), Mohit Choubey (Maulana Azad National Institute of Technology, India), Yogesh Kumar Gupta (University of Petroleum and Energy Studies, India & Maulana Azad National Institute of Technology, India)and Jigyendra Sen Yadav (Maulana Azad National Institute of Technology, India)
Copyright: 2026
Pages: 40
Source title:
AI and Digital Technologies Transforming Global Industries
Source Author(s)/Editor(s): Patricia Ordóñez de Pablos (The University of Oviedo, Spain), Xi Zhang (Tianjin University, China), Muhammad Anshari (Universiti Brunei Darussalam, Brunei)and Mohammad Nabil Almunawar (Universiti Brunei Darussalam, Brunei)
DOI: 10.4018/979-8-3373-6097-3.ch008
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Abstract
Acute and accurate disease diagnosis is of utmost significance in modern healthcare but is generally not possible with conventional imaging techniques. As AI-based techniques offer higher diagnostic accuracy and speed, their adoption is increasingly becoming vital. Through automated processing, recent advances in deep learning have revolutionized medical imaging, enhancing anomaly identification, organ segmentation, and disease detection. Convolutional neural networks (CNNs), generative adversarial networks (GANs), transformer models, hybrid models, and more are introduced in this chapter. Revolutionary methods like Federated Learning (FL) and Self-Supervised Learning (SSL) are also discussed, along with their usage in different imaging modalities. The chapter also discusses issues including data sparsity, model interpretability, and ethics. A detailed case study on the application of transfer learning for medical image classification also showcases the capability of artificial intelligence to augment clinical diagnosis.
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