Title: An optimised InceptionResNetV2 model for breast cancer histopathology image classification

Authors: I. Keren Evangeline; J. Glory Precious; C.D. Anand; S.P. Angeline Kirubha

Addresses: Department of Biomedical Engineering, SRM Institute of Science and Technology, Kattankulathur – 603203, India; Department of Medical Electronics, SRM Valliammai Engineering College, Kattankulathur – 603203, India ' Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur – 603203, India; Department of Biomedical Engineering, Rajalakshmi Engineering College, Thandalam – 602105, India ' Department of Pathology, SRM Medical College Hospital and Research Centre, SRM Institute of Science and Technology, Kattankulathur – 603203, India ' Department of Biomedical Engineering, SRM Institute of Science and Technology, Kattankulathur – 603203, India

Abstract: Breast cancer usually develops in women due to uncontrolled cell division. The clinical gold standard for diagnosing this disease is breast histopathology. Automating breast cancer detection saves time and aids pathologists. Deep learning is vital here. This study investigates utilising convolutional neural networks and transfer learning to identify breast cancer from histopathological image patches of all magnifications. Thus, an optimised deep learning model for breast cancer image classification was created by adding and modifying InceptionResNetV2 layers. Transfer learning was used to train and fine-tune it. The model was then compared to VGG-16, DenseNet-121, and original InceptionResNetV2 networks. The optimised InceptionResNetV2 model outperforms all other models with images of all magnification factors. For 400X magnification image classification, the optimised InceptionResNetV2 model has the maximum accuracy of 98%. Hence, the model predicts benign and malignant cancer image patches more accurately.

Keywords: image patches; optimised InceptionResNetV2; deep learning; diagnosis; histopathology; transfer learning; breast cancer.

DOI: 10.1504/IJBRA.2025.149727

International Journal of Bioinformatics Research and Applications, 2025 Vol.21 No.5, pp.463 - 480

Received: 27 Sep 2023
Accepted: 22 Mar 2024
Published online: 11 Nov 2025 *

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