Proposed Cnn Model For Breast Cancer Detection
DOI:
https://doi.org/10.29304/jqcsm.2025.17.22191Keywords:
CNN, Breast Cancer, Deep Learning, Data augmentationAbstract
lead to a longer lifespan. Breast cancer is a leading cause of death among women worldwide. In this study, we designed a proposed convolutional neural network (CNN) model that consisting of 39 layers. The model begins with feature extraction from input mammogram images using a series of convolutional layers, batch normalization, ReLU activation functions, and max-pooling layers. This is followed by six fully connected layers for classification. Overall, the proposed model includes a three-layer feature extractor and a two-layer decision-making module designed specifically for breast cancer detection. The model achieved impressive performance metrics, with an accuracy of 98%, precision of 95%, recall of 96%, and an F-score of 96.4% if number of epoch equal to 5 and 100% if epoch equal to 10.
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E. A. M. Sanjay S Tippannavar , Yashwanth S D, Gayathri S, “Early Detection of Breast Cancer using Versatile Techniques - A StudyNo Title,” J. Innov. Image Process., vol. 5, no. 3, pp. 270–289, doi:10.36548/jiip2023..3.004, 2023.
Y. Sun, Z. Zhu, and B. H. S. Asli, “Automated Classification and Segmentation and FeatureExtraction from Breast Imaging Data,” electronics, vol. 13, no. 19, https://doi.org/10.3390/electronics13193814, 2024.
N. Premanand et al., “Performance Evaluation of Convolutional Neural Networks for Stellar Image Classification: A Comparative Study,” IEEE, 2023, doi: https://doi.org/10.1109/ICDSNS58469.2023.10244794.
R. Yadav, S. K. Maurya, S. Sharma, S. G. -, and M. Agrawal, “Breast Cancer Detection using CNN,” Int. J. Multidiscip. Res., vol. 6, no. 3, https://doi.org/10.36948/ijfmr.2024.v06i03.21254, 2024.
E. Oncu, “ntegrating CNNs and ANNs: A Comprehensive AI Framework for Enhanced Breast Cancer Detection and Diagnosis,” MDPI, vol. 1, 2024, doi: https://www.preprints.org/manuscript/202410.2282/v1.
Rodrigues, Paulo Sergio (2017), “Breast Ultrasound Image”, Mendeley Data, V1, doi: 10.17632/wmy84gzngw.1.
https://data.mendeley.com/datasets/wmy84gzngw/1]..
H. Talebi and P. Milanfar, “Learning to Resize Images for Computer Vision Tasks,” arXiv Comput. Vis. Pattern Recognit., https://doi.org/10.48550/arXiv.2103.09950, 2021.
L. Rossi, A. Karimi, and A. Prati, “A novel Region of Interest Extraction Layer for Instance Segmentation,” arXiv Comput. Vis. Pattern Recog..., https://doi.org/10.1109/ICPR48806.2021.9412258, 2020.
Y. He and X. Xu, “Neural Network Algorithms in Upgrading Intelligent Process Design Systems,” 2024 Int. Conf. Data Sci. Netw. Secur., pp. 1–5, doi: 10.1109/ICDSNS62112.2024.1069124, 2024.
C. Miller, T. Portlock, D. M. Nyaga, and J. M. O’Sullivan, “A review of model evaluation metrics for machine learning in genetics and genomics,” Front. Bioinform., vol. 4, https://doi.org/10.3389/fbinf.2024.1457619, , 2024.
M. Khayatkhoei and W. AbdAlmageed, “Emergent Asymmetry of Precision and Recall for Measuring Fidelity and Diversity of Generative Models in High Dimensions,” Proc. Mach. Learn. Res., vol. 202, pp. 16326–16343, https://doi.org/10.48550/arXiv.2306.09618, 2023.
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Copyright (c) 2025 Mustafa A. Naser
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