Work

Breast Cancer Detection in Thermographic Images via Diffusion-Based Augmentation and Nonlinear Feature Fusion

Year: 2025

Type: conference-paper

Abstract: Data scarcity hinders deep learning for medical imaging. We propose a framework for breast cancer classification in thermograms that addresses this using a Diffusion Probabilistic Model (DPM) for data augmentation. Our DPM-based augmentation is shown to be superior to both traditional methods and a ProGAN baseline. The framework fuses deep features from a pre-trained ResNet-50 with handcrafted nonlinear features (e.g., Fractal Dimension) derived from U-Net segmented tumors. An XGBoost classifier trained on these fused features achieves 98.0% accuracy and 98.1% sensitivity. Ablation studies and statistical tests confirm that both the DPM augmentation and the nonlinear feature fusion are critical, statistically significant components of this success. This work validates the synergy between advanced generative models and interpretable features for creating highly accurate medical diagnostic tools.

Source: IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...)

DOI: 10.1109/bhi67747.2025.11269465

Authors: Sepehr Salem, M. Moein Esfahani, Jingyu Liu, Vince Calhoun

Institution: Center for Translational Research in Neuroimaging and Data Science

Language: English

Topic: Infrared Thermography in Medicine

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Science Foundation, National Institutes of Health