Breast Cancer Detection and Classification using Deep Learning Xception Algorithm

Breast Cancer (BC) is one of the leading cause deaths worldwide. Approximately 10 million people pass away internationally from breast cancer in year 2020. a fatal disease and very popular among women globally. It ranked fourth diseases different cancers, for example colorectal cancer, cervical brain tumors. Furthermore, number new cases anticipated to upsurge by 70% next twenty years. Consequently, early detection precise diagnosis plays an essential part enhancing improving survival rate patients 30 50%. Through advances technology healthcare, deep learning takes significant role handling inspecting great X-ray, Magnetic Resonance Imaging (MRI), computed tomography (CT) images. The aim this study propose model detect classify cancers. cancers has eight classes cancers: benign adenosis, fibroadenoma, phyllodes tumor, tubular adenoma, malignant ductal carcinoma, lobular mucinous papillary carcinoma. dataset was collected Kaggle depository classification. measurement that used evaluation proposed includes: F1-score, recall, precision, accuracy. trained, validated tested using preprocessed dataset. results showed Precision (97.60%), Recall (97.60%) F1-Score (97.58%). This indicates models are suitable detecting classifying precisely.