Constraint-Embedded Deep Learning for Breast Cancer Diagnosis Using Thermogram Images: An Experimental Study

Artificial intelligence (AI) breakthroughs have greatly changed medical diagnostics, especially in disease early detection like that of breast cancer. Thermography, a non-invasive imaging method, offers a new hope against conventional diagnostic techniques in the form of capturing temperature changes in breast tissue to facilitate early abnormality detection. Most AI algorithms, though, focus on prediction correctness at the expense of important domain-specific constraints necessary in clinical practice. This supervision tends to create models that work well in benchmark environments but do not work in actual medical settings because of demographics variation, imaging variability, and early threshold diagnosis. To address this deficiency, this paper embeds constraint reasoning into AI models to achieve high predictive accuracy and medical standard compliance. The proposed approach uses constraints that restrict outputs within valid ranges, prevent risk factor overestimation, ensure feature scaling consistency, and eliminate unrealistic values of medical parameters. By integrating such constraints, the model enhances robustness, reliability, and explainability, reducing symptomatic errors and clinical expectation compliance. This renders AI-based diagnostic tools clinically relevant, improving decision-making, and consequently resulting in more accurate and reliable breast cancer diagnosis in medical practice.