• Oluwasegun A. Abioye a Directorate of Information and Communication Technology, Nigerian Defence Academy, Kaduna, Nigeria
  • Abraham E. Evwiekpaefe Department of Computer Science, Nigerian Defence Academy, Kaduna, Nigeria
  • Awujoola J. Awujoola Directorate of Information and Communication Technology, Nigerian Defence Academy, Kaduna,



Histopathology, Breast cancer, image classification, EfficientNetV2


In the field of breast cancer diagnosis, the precise classification of benign images plays a pivotal role in ensuring effective patient care. This research undertakes a detailed examination of EfficientNetV2 models, specifically focusing on their ability to discern benign histopathology breast cancer images. The dataset were carefully curated to include diverse benign cases such as adenosis, fibroadenoma, phyllodes_tumor, and tubular_adenoma of image level for 40X magnification factor underwent thorough preprocessing before being divided into training and testing sets. Various variants of the EfficientNetV2 model—EfficientNetV2B0, EfficientNetV2B1, EfficientNetV2B2, EfficientNetV2B3, EfficientNetV2S, EfficientNetV2M, and EfficientNetV2L—were trained on the designated dataset. The performance evaluation shows the intricacies of the efficiency of each model. Notably, EfficientNetV2L emerged as a standout performer, boasting impressive metrics such as Accuracy (0.97), Precision (0.97), Recall (0.97), F1-score (0.97). These findings underscore the potential of EfficientNetV2L as a robust tool for accurately discerning benign histopathology breast cancer images. This study contributes significant insights to the field of breast cancer diagnostics, particularly addressing the critical task of classifying benign cases accurately. The gained insights pave the way for improved decision-making in assessments, ultimately enhancing the overall efficacy of breast cancer diagnosis.


Anastasiadi, Z., Lianos, G. D., Ignatiadou, E., Harissis, H. V., & Mitsis, M. (2017). Breast cancer in young women: an overview. Updates in surgery, 69, 313-317.

Araújo, T., Aresta, G., Castro, E., Rouco, J., Aguiar, P., Eloy, C., Polónia, A., Campilho, A., & Mendonça, A. M. (2017). Classification of breast cancer histology images using convolutional neural networks. PLoS One, 12(6), e0177544.

Aresta, G., Araújo, T., Kwok, S., Chennamsetty, S. S., Safwan, M., Alex, V., ... & Aguiar, P. (2019). Bach: Grand challenge on breast cancer histology images. Medical image analysis, 56, 122-139.

Bardou, D., Zhang, K., & Ahmad, S. M. (2018). Classification of breast cancer based on histology images using convolutional neural networks. IEEE Access, 6, 24680-24693.

Bay, H., Tuytelaars, T., & Gool, L. V. (2006). SURF: Speeded Up Robust Features. In Proceedings of the Computer Vision—ECCV 2006, Lecture Notes in Computer Science, Graz, Austria, 7–13 May 2006, 404–417.

Bayramoglu, N., Kannala, J., & Heikkilä, J. (2016). Deep learning for magnification independent breast cancer histopathology image classification. In 2016 23rd International Conference on Pattern Recognition (ICPR) (pp. 2440-2445).

Behar, N., & Shrivastava, M. (2022). ResNet50-Based Effective Model for Breast Cancer Classification Using Histopathology Images. CMES-Computer Modeling in Engineering & Sciences, 130(2).

Dimitriou, N., Arandjelović, O., & Caie, P. D. (2019). Deep learning for whole slide image analysis: an overview. Frontiers in Medicine, 6, 264.

Domingues, I., Pereira, G., Martins, P., Duarte, H., Santos, J., & Abreu, P. H. (2020). Using deep learning techniques in medical imaging: A systematic review of applications on CT and PET. Artificial Intelligence Review, 53, 4093-4160.

Doyle, S., Agner, S., Madabhushi, A., Feldman, M., & Tomaszewski, J. (2008). Automated grading of breast cancer histopathology using spectral clustering with textural and architectural image features. In 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro (pp. 496-499). IEEE.

Hameed, Z., Zahia, S., Garcia-Zapirain, B., Aguirre, J. J., & Vanegas, A. M. (2020). Breast cancer histopathology image classification using an ensemble of deep learning models. Sensors, 20(16), 4373.

Hirra, I., Ahmad, M., Hussain, A., Ashraf, M. U., Saeed, I. A., Qadri, S. F., ... & Alfakeeh, A. S. (2021). Breast cancer classification from histopathological images using patch-based deep learning modeling. IEEE Access, 9, 24273-24287.

Joseph, A. A., Abdullahi, M., Junaidu, S. B., Ibrahim, H. H., & Chiroma, H. (2022). Improved multi-classification of breast cancer histopathological images using handcrafted features and deep neural network (dense layer). Intelligent Systems with Applications, 14, 200066.

Loukas, C., Kostopoulos, S., Tanoglidi, A., Glotsos, D., Sfikas, C., & Cavouras, D. (2013). Breast cancer characterization based on image classification of tissue sections visualized under low magnification. Computational and Mathematical Methods in Medicine.

Lowe, D. G. (1999, September). Object recognition from local scale-invariant features. In Proceedings of the seventh IEEE international conference on computer vision (Vol. 2, pp. 1150-1157). Ieee.

Moghbel, M., Ooi, C. Y., Ismail, N., Hau, Y. W., & Memari, N. (2020). A review of breast boundary and pectoral muscle segmentation methods in computer-aided detection/diagnosis of breast mammography. Artificial Intelligence Review, 53, 1873-1918.

Murtaza, G., Shuib, L., Abdul Wahab, A. W., Mujtaba, G., Nweke, H. F., et al. (2020). Deep learning-based breast cancer classification through medical imaging modalities: State of the art and research challenges. Artificial Intelligence Review, 53, 1655-1720.

Ojala, T., Pietikainen, M., & Maenpaa, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 971–987.

Pavithra, S., Vanithamani, R., & Justin, J. (2020). Computer-aided breast cancer detection using ultrasound images. Materials Today: Proceedings, 33, 4802-4807.

Robertson, S., Azizpour, H., Smith, K., & Hartman, J. (2018). Digital image analysis in breast pathology—from image processing techniques to artificial intelligence. Translational Research, 194, 19-35.

Saha, M., Chakraborty, C., & Racoceanu, D. (2018). Efficient deep learning model for mitosis detection using breast histopathology images. Computerized Medical Imaging and Graphics, 64, 29-40.

Spanhol, F. A., Oliveira, L. S., Petitjean, C., & Heutte, L. (2015). A dataset for breast cancer histopathological image classification. IEEE Transactions on Biomedical Engineering, 63(7), 1455-1462.

Tan, M., & Le, Q. (2021). Efficientnetv2: Smaller models and faster training. In Proceedings of the International Conference on Machine Learning (ICML), July 2021 (pp. 10096-10106). PMLR

Vesal, S., Ravikumar, N., Davari, A., Ellmann, S., & Maier, A. (2018). Classification of breast cancer histology images using transfer learning. In Image Analysis and Recognition: 15th International Conference, ICIAR 2018, Póvoa de Varzim, Portugal, June 27–29, 2018, Proceedings (pp. 812-819). Springer International Publishing.

Wakili, M. A., Shehu, H. A., Sharif, M. H., Sharif, M. H. U., Umar, A., Kusetogullari, H., ... & Uyaver, S. (2022). Classification of breast cancer histopathological images using DenseNet and transfer learning. Computational Intelligence and Neuroscience, 2022.

Wang, K., Franch-Expósito, S., Li, L., Xiang, T., Wu, J., & Ren, G. (2020). 34P Comprehensive clinical and molecular portraits of grade 3 ER+ HER-breast cancer. Annals of Oncology, 31, S27.

Wang, P., Wang, J., Li, Y., Li, P., Li, L., & Jiang, M. (2021). Automatic classification of breast cancer histopathological images based on deep feature fusion and enhanced routing. Biomedical Signal Processing and Control, 65, 102341.

World Health Organization. (2018). Latest global cancer data: Cancer burden rises to 18.1 million new cases and 9.6 million cancer deaths in 2018. International Agency for Research on Cancer, Geneva, pp. 1-4.

Zewdie, E. T., Tessema, A. W., & Simegn, G. L. (2021). Classification of breast cancer types, sub-types and grade from histopathological images using deep learning technique. Health and Technology, 11(6), 1277-1290.

Zhang, B. (2011, October). Breast cancer diagnosis from biopsy images by serial fusion of Random Subspace ensembles. In 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI) (Vol. 1, pp. 180-186). IEEE.

Zhou, Y., Zhang, C., & Gao, S. (2022). Breast cancer classification from histopathological images using resolution adaptive network. IEEE Access, 10, 35977-35991.




How to Cite

Abioye, O. A., Evwiekpaefe, A. E., & Awujoola, A. J. (2024). PERFORMANCE EVALUATION OF EFFICIENTNETV2 MODELS ON THE CLASSIFICATION OF HISTOPATHOLOGICAL BENIGN BREAST CANCER IMAGES. Science Journal of University of Zakho, 12(2), 208–214.



Science Journal of University of Zakho