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    ARTICLE

    Automated Grading of Breast Cancer Histopathology Images Using Multilayered Autoencoder

    Shakra Mehak1, M. Usman Ashraf2, Rabia Zafar3, Ahmed M. Alghamdi4, Ahmed S. Alfakeeh5, Fawaz Alassery6, Habib Hamam7, Muhammad Shafiq8,*

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3407-3423, 2022, DOI:10.32604/cmc.2022.022705

    Abstract Breast cancer (BC) is the most widely recognized cancer in women worldwide. By 2018, 627,000 women had died of breast cancer (World Health Organization Report 2018). To diagnose BC, the evaluation of tumours is achieved by analysis of histological specimens. At present, the Nottingham Bloom Richardson framework is the least expensive approach used to grade BC aggressiveness. Pathologists contemplate three elements, 1. mitotic count, 2. gland formation, and 3. nuclear atypia, which is a laborious process that witness's variations in expert's opinions. Recently, some algorithms have been proposed for the detection of mitotic cells, but nuclear atypia in breast cancer… More >

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