Business Faculty Publications
Document Type
Article
Publication Date
5-27-2025
Publication Title
IEEE Access
Keywords
Breast cancer, Cancer, Accuracy, Tumors, Feature extraction, Computational modeling, Imaging, Histopathology, Deep learning, Invasive Ductal Carcinoma, EfficientNetV2-B3
Disciplines
Analytical, Diagnostic and Therapeutic Techniques and Equipment | Biotechnology | Business
Abstract
Breast cancer is the second leading cause of mortality among women worldwide, highlighting the need for efficient histopathology-based screening methods for early diagnosis. This study introduces HistoDX, a deep learning framework to classify Invasive Ductal Carcinoma (IDC) using 277,524 histopathology image patches (50x50 pixels) from Paul Mooney’s IDC dataset on Kaggle, comprising No Cancer and IDC(+) classes. HistoDX employs a preprocessing pipeline with normalization, data augmentation, and class balancing via oversampling and weighted loss to address the class imbalance. A customized convolutional neural network, built on EfficientNetV2-B3 with additional layers, achieves 97% accuracy and a 0.91 ROC-AUC score on the test set. Validation on BreakHis (97% accuracy) and BACH (90% accuracy) datasets confirm generalizability, though detecting minority IDC(+) cases remains challenging. Low training and test losses underscore reliability. HistoDX empowers pathologists by enhancing diagnostic efficiency and minimizing subjectivity through effective class imbalance mitigation. Its robust performance across diverse datasets like BreakHis and BACH suggests readiness for clinical integration. Future research into advanced augmentation techniques, ensemble models, and whole-slide image analysis could further optimize accuracy, sensitivity, and scalability, paving the way for broader adoption in precision oncology.
Recommended Citation
Arshad, WIshal; Masood, Tehreem; Shahzad, H. M.; Ahmed, Hassan A.; Ahmed, Syed Hamza; and Khushi, Hafiz Muhammad Tayyab, "HistoDX: Revolutionizing Breast Cancer Diagnosis Through Advanced Imaging Techniques" (2025). Business Faculty Publications. 359.
https://engagedscholarship.csuohio.edu/bus_facpub/359
DOI
10.1109/ACCESS.2025.3574210
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
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Analytical, Diagnostic and Therapeutic Techniques and Equipment Commons, Biotechnology Commons, Business Commons
