Ouf, MahmoudAbdul-Hamid, YasserMohammed, Ammar2023-04-082023-04-082023-03http://repository.msa.edu.eg/xmlui/handle/123456789/5530An Enhanced Deep Learning Approach for Breast Cancer Detection in Histopathology Images Mahmoud Ouf, Yasser Abdul-Hamid & Ammar Mohammed Conference paper First Online: 01 March 2023 129 Accesses Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT,volume 164) Abstract Breast cancer is defined as abnormal cellular proliferation in the breast. The most common kind of cancer that affects the breast and causes mortality in women is invasive ductal carcinoma (IDC). As a result, early diagnosis and prognosis have become critical to maximize survival and minimize mortality. Mammograms, computerized tomography (CT) scans, and ultrasounds are among the breast cancer tests available. On the other hand, histopathology evaluation with a biopsy is regarded as one of the most trustworthy techniques for determining if suspicious lesions are malignant. This paper proposes an enhanced approach for classifying breast tumors using a proposed CNN model that we named (CancerNet). We evaluate the proposed model on a benchmark dataset containing 277,524 patches. Compared to several types of CNN-based models, our proposed model has achieved accuracy, Area Under Curve (AUC), precision, recall, and F1-score of 86%, 92%, 81%, 84%, and 83%, respectively, outperforming the previous work on the same benchmark.en-USDeep learningConvolutional neural networkDepthwise separable networkCancer classificationInvasive ductal carcinoma (IDC)Article