Multiclassification Model of Histopathological Breast Cancer Based on Deep Neural Network

• Published in: 2022 19th International Multi-Conference on Systems, Signals & Devices (SSD) pp. 1105 - 1111
• Main Authors: Mohammed, Faris E., Zghal, Nadia Smaoui, Aissa, Dalinda Ben, El-Gayar, M. M.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 06.05.2022
• Subjects: Breast cancer; Coherence; Deep Convolutional Network; Deep learning; Image color analysis; Neural networks; Performance evaluation; Semantic segmentation; Smart Multiclassification
• ISSN: 2474-0446
• DOI: 10.1109/SSD54932.2022.9955814

Breast cancer is one of the most important diseases that lead to death, according to the reports of the World Health Organization. Reports also indicated that breast cancer affects women more than men. Late or wrong diagnosis leads to a deterioration in the patient's condition and may lead to death. Therefore, automated multiple classifications using machine learning of breast cancer from histopathological images plays a major role in the early diagnosis of the disease. Multiple classifications of breast cancer is the identification of tumors such as Carcinoma, Fibroadenoma, Cysts, etc. However, this type of classification faces many challenges such as extracting subtle differences between the binary classes of tumors and whether they are benign or malignant, strong coherence of tumor cells and widespread color heterogeneity. Therefore, we propose in this paper the use of a sophisticated model that works on semantic segmentation and extracting distinctive patterns and classifying them using a deep neural network. The structured deep learning model performed impressively (average accuracy of 98.7 % ) on a large-scale data set, demonstrating the power of our method in providing an effective tool for classifying multiple breast cancers in clinical settings.