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Decoding AI Integrated Clinical Practice: A CNN-Based Intelligent Learning Approach for Medical Image Analytic Cases
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Author(s): Rudra Kalyan Nayak (VIT Bhopal University, India), Nilamadhab Mishra (VIT Bhopal University, India), Ashwini Kumar Pradhan (Galgotias University, India)and Ramamani Tripathy (Chitkara University, India)
Copyright: 2026
Pages: 34
Source title:
AI-Driven Strategies for Sustainable Guest Experience in Intelligent Hospitality
Source Author(s)/Editor(s): Ahmed K. Elnagar (Taibah University, Saudi Arabia), Abdelkader Mohamed Sghaier Derbali (Taibah University, Saudi Arabia)and Ahmad Mohammad Herzallah (Al-Quds University, Palestine)
DOI: 10.4018/979-8-3373-8197-8.ch009
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Abstract
Medical imaging, which provides a non-invasive window into the complex mechanisms of the human body, has completely transformed the healthcare sector. The integration of deep learning techniques into medical image analysis is the primary focus of this work, which describes advances in segmentation, classification, and disease diagnosis tasks. After outlining traditional approaches and their shortcomings, this investigation examines convolutional neural networks (CNNs)—the underlying architecture for most deep learning applications in this discipline. The paper discusses applications across various imaging modalities, including CT, MRI, ultrasound, and X-ray, highlighting real-world achievements, challenges, and the path to clinical adoption. Emphasis is placed on the importance of advanced approaches, such as transfer learning, data augmentation, and explainable AI, in addressing challenges. This exploration also highlights the ethical and regulatory aspects, emphasizing the importance of fairness, transparency, and robustness in clinical applications.
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