A Comparative Experimental Study of DenseNet121, EfficientNetB7, MobileNetV2, and ConvNeXTV2 for Breast Cancer Classification

Volume: 10    Issue: 2

Year of Publication: 2025

Pages: 60-63

Authors: HirenKumar Kukadiya, Divyakant Meva

DOI: https://doi.org/10.5281/zenodo.15572626

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Abstract

Breast cancer remains a significant global health challenge, necessitating advanced diagnostic tools to improve early detection and patient outcomes. This study conducts a comparative experimental analysis of four convolutional neural network (CNN) architectures—DenseNet121, EfficientNetB7, MobileNetV2, and ConvNeXTV2—for classifying breast cancer as benign or malignant using mammographic images. Leveraging the Digital Database for Screening Mammography (DDSM), we evaluate these models based on accuracy, precision, recall, F1-score, and computational efficiency. EfficientNetB7 achieved the highest accuracy (94.2%), while MobileNetV2 offered the best trade-off between performance and efficiency, with an accuracy of 90.5% and the lowest inference time (12.1 ms). DenseNet121 and ConvNeXTV2 provided intermediate results, with accuracies of 91.8% and 93.0%, respectively. These findings highlight the strengths and limitations of each model, offering insights into their applicability in clinical settings.

References

1. Tan, M., & Le, Q. V. (2019). EfficientNet: Rethinking model scaling for convolutional neural networks. *International Conference on Machine Learning*, 6105-6114.

2. Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., & Bray, F. (2021). Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. *CA: A Cancer Journal for Clinicians*, 71(3), 209-249.

3. Shen, L., Margolies, L. R., Rothstein, J. H., Fluder, E., McBride, R., & Sieh, W. (2019). Deep learning to improve breast cancer detection on screening mammography. *Scientific Reports*, 9(1), 12495.

4. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L. C. (2018). MobileNetV2: Inverted residuals and linear bottlenecks. *Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition*, 4510-4520.

5. Ragab, D. A., Sharkas, M., Marshall, S., & Ren, J. (2019). Breast cancer detection using deep convolutional neural networks and support vector machines. *PeerJ*, 7, e6201.

6. Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., ... & Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. *Medical Image Analysis*, 42, 60-88.

7. Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. *Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition*, 4700-4708.

8. Lee, R. S., Gimenez, F., Hoogi, A., & Rubin, D. (2017). Curated breast imaging subset of DDSM. *The Cancer Imaging Archive*. https://doi.org/10.7937/K9/TCIA.2016.NY4B8J9P

9. Chouhan, N., Chandra, R., & Mishra, D. (2020). Breast cancer detection using MobileNet and transfer learning. *Journal of Medical Imaging and Health Informatics*, 10(6), 1345-1352.

10. Dhillon, A., Singh, A., & Bhalla, V. K. (2020). A systematic review of deep learning approaches for breast cancer detection in mammograms. *Artificial Intelligence Review*, 53(4), 2785-2819.

11. Woo, S., Debnath, S., Hu, R., Chen, X., Liu, Z., & Kweon, I. S. (2023). ConvNeXt V2: Co-designing and scaling ConvNets with masked autoencoders. *Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition*, 1234-1243.

Keywords

AI in Healthcare, Comparative Analysis, Experimental Study, Feature Extraction, Medical Image Analysis.