Breast Cancer Detection Through Deep Learning and Breast Thermography: A Study of Review
Keywords:
Deep Learning, Breast Thermography, Thermal Imaging, Breast Cancer Detection, Medical ImagingAbstract
Breast cancer is an alarming worldwide health concern, and early detection is crucial for improving patient outcomes. This study explores the application of deep learning algorithms in breast thermography, a non-invasive and radiation-free imaging tech-nique, to enhance diagnostic accuracy. This research synthesizes peer-reviewed lit-erature from 2020 to 2025, focusing on various deep learning architectures, including CNNs, GANs, RNNs, U-Net, and transfer learning, in relation to thermographic da-tasets like DMR-IR and Visual DMR. The methodology employs a structured approach encompassing literature searches, criteria for inclusion and exclusion, data extraction, and synthesis. The findings indicate that deep learning significantly enhances seg-mentation, classification, and anomaly detection in thermal breast images, frequently surpassing traditional diagnostic techniques. While accuracy rates are promising, challenges persist, such as limitations in datasets, variability in images, and a lack of standardization. This study highlights the potential of AI-enhanced thermography as a cost-effective and scalable method for breast cancer screening, while also identifying key areas for further research to enhance generalizability and clinical application.
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