Transformative Role of Advanced Neural Computation in Clinical Image Diagnostics: A Review of Key Concepts and Applications.
Authors
Affiliations (13)
Affiliations (13)
- Department of Electrical and Electronics Engineering, Shri Ramswaroop Memorial University, Deva Road, Lucknow, Barabanki, Uttar Pradesh, India.
- Department of Computer Science and Applications, Sharda University, Greater Noida, Uttar Pradesh, India.
- Department of Forensic Science, School of Applied Science, OM Sterling Global University (OSGU), Hisar, Haryana, India.
- Department of Computer Science and Engineering, Shri Ramswaroop Memorial University, Barabanki, Uttar Pradesh, India.
- All India Institute of Ayurveda (AIIA), Government of India, New Delhi - 110076, India. Electronic address: [email protected].
- Computer Engineering Department, Sardar Vallabhbhai Patel Institute of Technology (SVIT), Gujarat Technological University (GTU), Anand, Gujarat, India.
- Master of Public Health (MPH), Monroe University, Bronx, NY 10468, United States of America.
- Department of Computer Science, CHRIST University, Bangalore, Karnataka, India.
- Department of Computer Science and Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India.
- Department of Computer Science and Engineering, Bharati Vidyapeeth's College of Engineering, New Delhi - 110063, India.
- All India Institute of Ayurveda (AIIA), Government of India, New Delhi - 110076, India.
- Department of Forensic Science, RIMT University, Mandi Gobindgarh, Punjab, India.
- Department of Pharmaceutical Science, Chameli Devi Institute of Pharmacy, Indore, Madhya Pradesh, India.
Abstract
Medical imaging plays a crucial role in modern diagnostic practices, but traditional techniques often face limitations in accuracy, efficiency, and scalability. The emergence of deep learning (DL) has led to significant improvements that are transforming this field. This review discusses how DL algorithms are enhancing diagnostic imaging by improving accuracy, enabling automated analysis, and supporting personalized treatment plans. It focuses on key deep learning (DL) frameworks, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). The review examines their applications in important medical imaging tasks such as image classification, segmentation, reconstruction, and disease prediction. It also considers how DL techniques are integrated with tools like radiomics, data augmentation strategies, and predictive analytics models. DL methods have shown superior performance in detecting and classifying diseases like pneumonia, tuberculosis, and Alzheimer's. They also improve the quality and speed of imaging modalities such as MRI, CT, and ultrasound. Despite these advances, challenges remain in data availability, model interpretability, clinical validation, and ethical issues related to bias and privacy. Addressing these challenges is essential for the successful clinical use of DL in medical imaging. This review ends with suggestions for future directions and best practices for ethically and practically integrating DL technologies into routine healthcare.