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Application of Deep Learning System Technology in Identification of Women's Breast Cancer. Medicina 2023, 59, 487 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)medicina59030487  \nAcademic Editor: Udo Jeschke  \nReceived: 16 January 2023  \nRevised: 30 January 2023  \nAccepted: 3 February 2023  \nPublished: 1 March 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Educational Technology, College of Education, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia  \n2 Applied College, Curriculum and Instruction, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia  \n3 Information Technology College, Ajloun National University, Ajloun 26873, Jordan  \n4 Research Center, The University of Mashreq, Baghdad 11001, Iraq  \n* [Correspondence: malikbader2@gmail.com](Correspondence: malikbader2@gmail.com)  \nAbstract: Background and Objectives: The classification of breast cancer is performed based on its histological subtypes using the degree of differentiation. However, there have been low levels of intraand inter-observer agreement in the process. The use of convolutional neural networks (CNNs) in the field of radiology has shown potential in categorizing medical images, including the histological classification of malignant neoplasms. Materials and Methods: This study aimed to use CNNs to develop an automated approach to aid in the histological classification of breast cancer, with a focus on improving accuracy, reproducibility, and reducing subjectivity and bias. The study identified regions of interest (ROIs), filtered images with low representation of tumor cells, and trained the CNN to classify the images. Results: The major contribution of this research was the application of CNNs asa machine learning technique for histologically classifying breast cancer using medical images. The study resulted in the development of a low-cost, portable, and easy-to-use AI model that can be used by healthcare professionals in remote areas. Conclusions: This study aimed to use artificial neural networks to improve the accuracy and reproducibility of the process of histologically classifying breast cancer and reduce the subjectivity and bias that can be introduced by human observers. The results showed the potential for using CNNs in the development of an automated approach for the histological classification of breast cancer.  \nKeywords: deep learning; breast cancer; CNN; K-means algorithm; clustering segmentation  \n1. Introduction  \nBreast cancer, which is the most prevalent malignancy among women globally, continues to pose a significant challenge in terms of its impact on public health, particularly in Iraq, where it was estimated to account for 60,000 new cases in 2020 [1] . As the leading cause of death from malignant diseases in the Iraqi population, death from breast cancer is primarily due to its metastatic spread [2,3] . Despite extensive research, the underlying determinants that contribute to the metastasis of neoplastic cells in breast carcinoma remain largely elusive [4] .  \nPrognostic factors play a critical role in the clinical management of breast cancer and the selection of appropriate therapeutic regimens, with clinical and histological criteria, tumor markers, and the axillary stage of the disease being among the most widely used predictors [5] . The degree of tumor differentiation, as measured by the ","cbCaiviRjd2T3Ef9","https://ap.wps.com/l/cbCaiviRjd2T3Ef9","pdf",2117000,17,"English","# Abstract\n# Introduction","[{\"question\":\"What problem does the study target in breast cancer histological classification?\",\"answer\":\"It targets low intra- and inter-observer agreement when grading breast cancer histology, which can affect reproducibility and clinical decision-making.\"},{\"question\":\"How is the convolutional neural network (CNN) used in the proposed approach?\",\"answer\":\"The method identifies regions of interest, filters images with low tumor-cell representation, and trains the CNN to classify histological images.\"},{\"question\":\"What outcomes does the study report for the developed AI model?\",\"answer\":\"The research reports the development of a low-cost, portable, and easy-to-use AI model that can support healthcare professionals, especially in remote areas, by improving accuracy and reducing bias.\"}]","Application of Deep Learning System Technology in Identification of Women's Breast Cancer - Article | PDF",1790232117,43]