Breast Cancer Detection and Classification using Deep Learning Xception Algorithm

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

@article{Abunasser2022,
  title     = {Breast Cancer Detection and Classification using Deep Learning Xception Algorithm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {7},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Basem S. Abunasser and Mohammed Rasheed J. AL-Hiealy and Ihab S. Zaqout and Samy S. Abu-Naser},
  doi       = {10.14569/IJACSA.2022.0130729},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130729}
}