Application of Deep Learning System Technology in Identification of Women's Breast Cancer - Article

Sep 24, 2026

Uploaded by Finn

Deep learning for breast cancer classification addresses limited intra- and inter-observer agreement in histological grading based on differentiation. The study develops an automated convolutional neural network (CNN) pipeline that extracts regions of interest, filters images with low tumor-cell representation, and trains the model to classify breast cancer histology. Results support a low-cost, portable AI system designed to improve accuracy, reproducibility, and reduce subjectivity and bias for healthcare use in remote areas.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages17

Reading Time43 min

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Abstract

Introduction

medicina Article Application of Deep Learning System Technology in Identiﬁcation of Women's Breast Cancer Latefa Hamad Al Fryan 1, Mahasin Ibrahim Shomo 2 and Malik Bader Alazzam 3,4, * Citation: Al Fryan, L.H.; Shomo, M.I.; Alazzam, M.B. 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 Academic Editor: Udo Jeschke Received: 16 January 2023 Revised: 30 January 2023 Accepted: 3 February 2023 Published: 1 March 2023 Copyright: © 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/)) . 1 Department of Educational Technology, College of Education, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia 2 Applied College, Curriculum and Instruction, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia 3 Information Technology College, Ajloun National University, Ajloun 26873, Jordan 4 Research Center, The University of Mashreq, Baghdad 11001, Iraq * [Correspondence: malikbader2@gmail.com](Correspondence: malikbader2@gmail.com) Abstract: 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. Keywords: deep learning; breast cancer; CNN; K-means algorithm; clustering segmentation 1. Introduction Breast 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] . Prognostic 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

FAQs

What problem does the study target in breast cancer histological classification?

It targets low intra- and inter-observer agreement when grading breast cancer histology, which can affect reproducibility and clinical decision-making.

How is the convolutional neural network (CNN) used in the proposed approach?

The method identifies regions of interest, filters images with low tumor-cell representation, and trains the CNN to classify histological images.

What outcomes does the study report for the developed AI model?

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.

Sep 24, 2026

Uploaded by Finn