Dynamic Thermal Imaging and Frame-wise Deep Learning Analysis for Breast Cancer Diagnosis: A Comparative Study with Mammography
Keywords:
Dynamic Thermal Imaging, Deep Learning, Breast Cancer Detection, Mammography, CNN-LSTM, Temporal Analysis, DMR-IR, CBIS-DDSM, Medical Image Classification, AI-Based DiagnosticsAbstract
Timely identification of breast cancer plays a vital role in enhancing survival rates. While mammography is a widely adopted imaging technique, it often presents limitations related to accessibility, sensitivity, and radiation exposure—particularly in low-resource environments. In this study, we investigate the application of dynamic thermal imaging (DMR-IR) as a radiation-free, non-invasive alternative, utilizing a frame-wise deep learning strategy.
A comparative evaluation is conducted using two datasets: the DMR-IR thermal image sequences and the established CBIS-DDSM mammographic dataset. The proposed methodology integrates convolutional neural networks (CNN), transfer learning models including ResNet50 and EfficientNetB0, along with an optional CNN-LSTM architecture to model both spatial and temporal dynamics present in thermal image frames. Evaluation metrics—namely accuracy, precision, recall, F1-score, and AUC—demonstrate that the DMR-IR dataset yields superior classification performance across all tested models when compared to mammography.
The findings underscore the effectiveness of temporal thermal patterns in identifying malignancies and present a promising, scalable solution for early breast cancer screening, particularly in resource-constrained clinical settings. This work establishes the groundwork for advancing AI-driven diagnostic solutions based on physiological imaging data.
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