Application of Artificial Intelligence in Early Detection of Lung Cancer 1st Edition

Application of Artificial Intelligence in Early Detection of Lung Cancer (1st Edition) by Madhuchanda Kar, Jhilam Mukherjee, Amlan Chakrabarti, and Sayan Das offers a compelling, practical roadmap for harnessing AI to transform lung cancer diagnosis worldwide. Combining clinical insight with cutting-edge machine learning techniques, this book speaks directly to radiologists, oncologists, data scientists, medical students, and healthcare administrators seeking actionable solutions.

Begin with a clear exploration of the challenges in early lung cancer detection—low signal lesions, variable imaging quality, and diagnostic delays—and move into how AI-driven approaches like deep learning, radiomics, and computer-aided diagnosis can bridge those gaps. The authors present real-world case studies, step-by-step model development workflows, and best-practice evaluation metrics so readers can implement robust systems in hospital and research settings.

Practical chapters cover CT image preprocessing, feature extraction, predictive modeling, explainability, and integration into clinical workflows with attention to regulatory and ethical considerations. Global in scope, the text highlights implementations applicable to centers across India, Asia, Europe, and North America, emphasizing scalable solutions for both high-resource hospitals and resource-limited clinics.

Whether you’re building diagnostic tools, designing clinical trials, or shaping policy, this book equips you with the technical knowledge and clinical perspective to accelerate early detection efforts. Precise, well-illustrated, and grounded in current research, it’s an essential reference for anyone committed to improving patient outcomes through AI.

Add this authoritative title by Madhuchanda Kar, Jhilam Mukherjee, Amlan Chakrabarti, and Sayan Das to your professional library—an indispensable guide to the future of lung cancer diagnosis.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.