Classification of Thermographic Images for Breast Cancer Detection Based on Deep Learning
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As per statistics, around the world, breast cancer is the most common cancer, today it kills around 2.5 million people annually. Early diagnosis of this condition, along with accurate classifications promote more favorable outcomes for the people who already have it and save premature victims from developing it. Difficulties face cancer researchers with the task of differentiating between benign and malignant tumors, as well as making conclusions on mild and advanced breast cancer. In machine learning, using algorithms that are able to find and identify patterns is used for the detection of all cancers. However, as previously discussed, all of them center on "binary grouping" (cancer and no-cancer; benign and malignant). In this research, we propose a Computer-aided Diagnosis (CAD) method for diagnosis and classification of patients (beneath the computer-based DMR) into three distinct groups (cancer, no cancer, and non-cancerous) under database management. Three exceptional classifiers include Convolution Neural Network (CNN), Support Vector Machine (SVM), and Random Forest (RF).For the classification process, we investigate and study three powerful classifiers: Convolution Networks (SVM), and Random Forest (RF). We also review the effects of prior to the pre-processing of the mammogram images, which enables higher successful classification.