Thermal Imaging and Computational Intelligence Techniques in Disease Detection by Singh, Neha; Dargar, Shashi Kant; Birla, Shilpi;

Short description:

The text presents a compilation of research and methodologies at the intersection of thermal imaging, medical diagnostics, and artificial intelligence for non-invasive early disease detection, diagnosis, and monitoring across a spectrum of medical conditions.

Long description:

The text presents a compilation of research and methodologies at the intersection of thermal imaging, medical diagnostics, and artificial intelligence for noninvasive early disease detection, diagnosis, and monitoring across a spectrum of medical conditions. It highlights the importance of using the latest technologies to improve performance, productivity, efficiency, and security in healthcare without sacrificing reliability or accessibility. The structured chapters provide both foundational and advanced insights into the use of infrared thermography in breast thermogram analysis, osteoporosis detection, cancer research, diabetic neuropathy, oral cancer, and skin lesions, emphasizing how computational intelligence augments diagnostic precision.

This book:

• Covers the application of thermograms in the early detection of various diseases using faster and more efficient image processing and intelligent classification algorithms.


• Discusses the use of different image processing methods in thermal imaging applications to diagnose various medical issues.


• Focuses on implementing the latest artificial intelligence techniques including machine learning to automate the decisions based on thermograms to aid medical experts in quick and effective diagnosis with early detection of diseases.


• Presents an in-depth analysis of the application of infrared thermal imaging in the diagnosis of specific diseases like cancer, osteoporosis, skin lesions, and diabetic neuropathy focusing on the computational techniques.

It is primarily written for senior undergraduates, graduate students, and academic researchers in medicine, bioengineering, electrical engineering, electronics and communications engineering, computer science engineering, and biomedical engineering.

Table of Contents:

1. Harnessing Machine Learning for Breast Thermogram Analysis: Advancements in Early Detection and Diagnosis. 2. Automatic Detection of Osteoporosis using Explainable AI Techniques. 3. Ensemble Machine Learning classification for breast thermograms. 4. Transforming Cancer Research: Early Detection Using Thermal Imaging and Machine Learning. 5. Infrared Thermal Imaging In Diabetic Neuropathy. 6. Integration of Computational intelligence and Thermal Imaging for Enhanced Skin Lesion Diagnosis. 7. A comprehensive overview of advancing Oral Cancer Detection: The Role of Thermal Imaging and Artificial Intelligence. 8. Ocular Surface Temperature Measurements in Diabetic Retinopathy Affected Patients using Infrared Thermal Imaging. 9. From History to Modernity: Agnikarma Procedure and its Evaluation with Technical Methodologies. 10. A Comprehensive Framework for the Detection and Management of Diabetic Foot Ulcers Using Diverse Imaging Techniques. 11. Identification of Jaundice-affected and normal infants in NICUs from the clinical image dataset using deep learning models. 12. Thermogram Analysis from an Image Processing Perspective. 13. Convolutional Neural Networks for Early Disease Detection in AI-Powered Thermographic Imaging. 14. A Comprehensive Review of Machine Learning and Deep Learning Techniques for Early Detection of Alzheimer’s and Dementia