A Comprehensive Review of CNN-Based Thermal Signature Analysis for Advancements in Personalized Health Monitoring

Abstract

This review systematically examines the application of CNNs in thermal signature analysis, with a focus on their transformative role in advancing personalized health monitoring systems. The study commences with an overview of the theoretical underpinnings of CNN architectures and their efficacy in biomedical image processing, particularly in extracting discriminative features from thermographic data. A detailed analysis of current methodologies is presented, encompassing pre-processing techniques, model architectures, training paradigms, and performance metrics relevant to thermal imaging applications. The review further categorizes and evaluates recent research efforts that employ CNN-based approaches for clinical diagnostics, physiological monitoring, and anomaly detection using thermal imagery. Particular attention is given to the integration of thermal imaging with wearable technologies, emphasizing its potential to enable continuous, contactless, and real-time health monitoring in diverse environments. Additionally, the paper addresses key technical challenges—such as data heterogeneity, limited annotated datasets, and thermal noise—as well as emerging trends including multimodal fusion, transfer learning, and edge computing. The review concludes by outlining prospective research directions aimed at enhancing model generalization, interpretability, and deployment in real-world healthcare scenarios. This work highlights the critical role of CNN-driven thermal signature analysis in shaping the future of precision medicine and underscores the necessity for interdisciplinary research to accelerate clinical adoption.