Seyedreza Movahedi, Punitee Garyali, Iman Ranjbar
Background and objectives: Breast cancer is one of the most common cancers in women and 7% of breast cancer cases in the United States occur in women under the age of 40. Early diagnosis and intervention are essential not only for better patient prognosis, but also to reduce the ever increasing burden on the healthcare system across the globe. The current gold standard for breast cancer diagnosis, mammography, is limited in its capacity to detect breast cancer in early stages especially with younger women with dense breast tissue. Additionally, there is limited accessibility and affordability of mammography in low and middle income countries. Thermography, on the other hand, can detect cancer in very early stages and in this paper; we discuss an AI-powered thermography based breast cancer prediction tool.
Methods: The proposed method involves data pre-processing, data augmentation, a detailed training strategy, and a post-processing risk calculation step. The proposed algorithm was trained using 1600 images from breast thermography databases to detect abnormalities in the breast tissue.
Results: On our dataset, we obtained an accuracy of 93%, 95% precision with >90% specificity and sensitivity, which is a significant breakthrough in using thermography as a potential screening for breast cancer. Additionally, with the risk calculator, the model can predict the risk of developing breast cancer in the future.
Conclusion: The high accuracy of our proposed model and the risk prediction capabilities enable the AI-powered screening tool by AI Talos to become the computer aided diagnostic system that supports screening and early detection of breast cancer especially in younger population.
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