Breast Cancer Classification Using Convolutional Neural Network: A Systematic Literature Review

Dababat, Alya', Eleyan, Derar, Eleyan, Amna ORCID: https://orcid.org/0000-0002-2025-3027, Amer, Mohammed and Bejaoui, Tarek (2025) Breast Cancer Classification Using Convolutional Neural Network: A Systematic Literature Review. In: 2025 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1-7. Presented at International Conference on Smart Applications, Communications and Networking (SmartNets), 22 - 24 July 2025, Istanbul, Turkiye.

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Abstract

Breast cancer is a prevalent disease affecting millions of women around the world. A key factor in improving the outcome of patients with breast cancer is early detection and classification. The use of convolutional neural networks (CNNs) has shown promising results for the analysis of various medical images, including the classification of breast cancer. This paper presents an overview of the breast cancer classification problem and demonstrates how a CNN can be effectively utilized for this task. Additionally, numerous papers have been presented and compared in terms of CNN structures, datasets, images, and accuracy. Different CNN models have been found to be effective at detecting breast cancer, which affects its accuracy. It should be recognized, however, that the accuracy of this algorithm depends on both the size of the dataset and the number of images that are used. As a result, it can be concluded that the number of images, datasets, or even the CNN approach can be used case-by-case to have higher accuracy. Finally, the results of accuracy should be expanded based on the analysis of one parameter in upcoming research. As soon as the best accuracy has been achieved, additional parameters may be added.