An Improved ResNet Architecture for Accurate Patch-Level HER2 Overexpression Classification in Breast Cancer Tissue Images
Received: 14 May 2025 | Revised: 13 June 2025, 1 July 2025, and 11 July 2025 | Accepted: 13 July 2025 | Online: 6 October 2025
Corresponding author: Anupkumar Bongale
Abstract
With the advent of artificial intelligence, various computer-aided diagnostic systems are being developed to assist medical professionals. Deep learning techniques powered by convolutional neural networks seem promising for obtaining new insights into the onco-histopathology domain. Breast cancer is confirmed by histopathological analysis of Hematoxylin and Eosin (H&E)-stained breast tissue images. Finding the molecular subtype of breast cancer using Immunohistochemistry (IHC)-stained breast tissue is essential to decide on the correct treatment plan for a breast cancer patient. IHC staining is an expensive process that is very time-consuming and involves intra- and inter-observer subjectivity. This work attempts to find the Human Epidermal growth factor Receptor Two (HER2) molecular subtype from H&E-stained tissue images instead of using IHC-stained tissues. H&E-stained tissue image data from two separate sources are used to predict HER2 status. This study aimed to improve the accuracy of HER2 overexpression classification by modifying the architecture of the ResNet50 model by cascading it with a squeeze and excitation block and a depth-wise separable convolutional layer. The dataset comprises a combination of tissue image patches from a publicly available Warwick dataset and a real-world dataset collected from a hospital in Pune, India. The dataset is preprocessed and split into 60% train, 20% validation, and 20% test subsets. The proposed architecture with a modified ResNet50 network achieves the best patch-level HER2 classification accuracy of 98.04%, with class-specific test accuracy results for HER2 negative, HER2 equivocal, and HER2 positive scores being 97.73%, 99.70%, and 98.93%, respectively.
Keywords:
breast cancer, HER2, histopathology, H&E-staining, ResNet50, SE blocks, patchesDownloads
References
M. N. Gurcan, L. E. Boucheron, A. Can, A. Madabhushi, N. M. Rajpoot, and B. Yener, "Histopathological Image Analysis: A Review," IEEE Reviews in Biomedical Engineering, vol. 2, pp. 147–171, 2009.
D. J. Slamon, G. M. Clark, S. G. Wong, W. J. Levin, A. Ullrich, and W. L. McGuire, "Human Breast Cancer: Correlation of Relapse and Survival with Amplification of the HER-2/neuOncogene," Science, vol. 235, no. 4785, pp. 177–182, Jan. 1987.
A. C. Wolff et al., "Human Epidermal Growth Factor Receptor 2 Testing in Breast Cancer," Archives of Pathology & Laboratory Medicine, vol. 147, no. 9, pp. 993–1000, Sep. 2023.
F. Petrelli, G. Tomasello, S. Barni, V. Lonati, R. Passalacqua, and M. Ghidini, "Clinical and pathological characterization of HER2 mutations in human breast cancer: a systematic review of the literature," Breast Cancer Research and Treatment, vol. 166, no. 2, pp. 339–349, Nov. 2017.
E. Ferraro, J. Z. Drago, and S. Modi, "Implementing antibody-drug conjugates (ADCs) in HER2-positive breast cancer: state of the art and future directions," Breast Cancer Research, vol. 23, no. 1, Dec. 2021.
E. A. Rakha et al., "Updated UK Recommendations for HER2 assessment in breast cancer," Journal of Clinical Pathology, vol. 68, no. 2, pp. 93–99, Feb. 2015.
N. Naik et al., "Deep learning-enabled breast cancer hormonal receptor status determination from base-level H&E stains," Nature Communications, vol. 11, no. 1, Nov. 2020.
K. Ingale et al., "Efficient and Generalizable Prediction of Molecular Alterations in Multiple-Cancer Cohorts Using Hematoxylin and Eosin Whole Slide Images," Modern Pathology, vol. 38, no. 4, Apr. 2025, Art. no. 100691.
S. A. Joshi, A. M. Bongale, and A. M. Bongale, "Breast Cancer Detection from Histopathology Images using Machine Learning Techniques: A Bibliometric Analysis," Library Philosophy and Practice (e-journal), Art. no. 5376.
R. Gurumoorthy and M. Kamarasan, "Breast Cancer Classification from Histopathological Images using Future Search Optimization Algorithm and Deep Learning," Engineering, Technology & Applied Science Research, vol. 14, no. 1, pp. 12831–12836, Feb. 2024.
S. M. Shaaban, M. Nawaz, Y. Said, and M. Barr, "An Efficient Breast Cancer Segmentation System based on Deep Learning Techniques," Engineering, Technology & Applied Science Research, vol. 13, no. 6, pp. 12415–12422, Dec. 2023.
M. Tafavvoghi, L. A. Bongo, N. Shvetsov, L. T. R. Busund, and K. Møllersen, "Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review," Journal of Pathology Informatics, vol. 15, Dec. 2024, Art. no. 100363.
T. Qaiser et al., "HER2 challenge contest: a detailed assessment of automated HER2 scoring algorithms in whole slide images of breast cancer tissues," Histopathology, vol. 72, no. 2, pp. 227–238, Jan. 2018.
S. A. Joshi, A. M. Bongale, P. O. Olsson, S. Urolagin, D. Dharrao, and A. Bongale, "Enhanced Pre-Trained Xception Model Transfer Learned for Breast Cancer Detection," Computation, vol. 11, no. 3, Mar. 2023, Art. no. 59.
P. G. P, K. Senapati, and A. K. Pandey, "A Novel Decision Level Class-Wise Ensemble Method in Deep Learning for Automatic Multi-Class Classification of HER2 Breast Cancer Hematoxylin-Eosin Images," IEEE Access, vol. 12, pp. 46093–46103, 2024.
P. Jiao et al., "Prediction of HER2 Status Based on Deep Learning in H&E-Stained Histopathology Images of Bladder Cancer," Biomedicines, vol. 12, no. 7, Jul. 2024, Art. no. 1583.
T. Araújo et al., "Classification of breast cancer histology images using Convolutional Neural Networks," PLOS ONE, vol. 12, no. 6, Jun. 2017, Art. no. e0177544.
M. Macenko et al., "A method for normalizing histology slides for quantitative analysis," in 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Boston, MA, USA, Jun. 2009, pp. 1107–1110.
J. Hu, L. Shen, S. Albanie, G. Sun, and E. Wu, "Squeeze-and-Excitation Networks," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 42, no. 8, pp. 2011–2023, Aug. 2020.
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, "Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization," International Journal of Computer Vision, vol. 128, no. 2, pp. 336–359, Feb. 2020.
S. Kabir et al., "The utility of a deep learning-based approach in Her-2/neu assessment in breast cancer," Expert Systems with Applications, vol. 238, Mar. 2024, Art. no. 122051.
M. F. Mridha, Md. K. Morol, Md. A. Ali, and M. S. H. Shovon, "convoHER2: A Deep Neural Network for Multi-Stage Classification of HER2 Breast Cancer," AIUB Journal of Science and Engineering (AJSE), vol. 22, no. 1, pp. 53–81, May 2023.
D. Anand et al., "Deep Learning to Estimate Human Epidermal Growth Factor Receptor 2 Status from Hematoxylin and Eosin-Stained Breast Tissue Images," Journal of Pathology Informatics, vol. 11, no. 1, Jan. 2020, Art. no. 19.
Downloads
How to Cite
License
Copyright (c) 2025 Shubhangi Joshi, Pallavi Chaudhari, Anupkumar Bongale, Deepak Dharrao, Vivek Dugad, Anand Bhosale
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
