Breast Cancer Detection from Thermal Images Using Deep Learning Techniques
Keywords:
Disease Detection, Breast Cancer, CNN, Deep Learning, Thermal Images, InceptionV3, MobileNet, XceptionAbstract
Breast cancer is a common disease that many men and women face throughout their lives. Therefore, early diagnosis is the most effective and reliable tool to effectively treat cancer. Hence, there is a need to help doctors diagnose this disease in less time so that we can reduce deaths. Recently, several methods have been used to classify cancer and determine the stage of this serious disease, including self-examination, clinical examination, and imaging techniques. Furthermore, several studies have used deep learning techniques to evaluate rapid datasets for cancer detection from thermal images of breast tumors. Hence, in this work, three pre-trained CNN models, namely InceptionV3, MobileNet, and Xception, were applied to classify breast images into cancerous tumors (Malignant) or non-cancerous tumors (Benign) from the thermal images according to DMR-IR criteria. The experiment results demonstrated that the suggested models attained excellent outcomes and can be efficiently utilized to classify breast cancer. More precisely, our suggested Xception model achieved the best results with 100% for the Accuracy, Precision, Recall, F1-Score, and the ROC curve.
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