Business Faculty Publications

Document Type

Article

Publication Date

5-27-2025

Publication Title

IEEE Access

Keywords

Breast Cancer, Breast Cancer Diagnosis, Neural Network, Weight Loss, Image Analysis, Deep Learning, Convolutional Neural Network, Ensemble Model, Class Imbalance, Training Loss, Image Patches, Digital Pathology, Histopathological Images, False Positive Rate, Negative Predictive Value, Binary Classification, Cancer Screening, Deep Learning Models, Patterns In Data, Transfer Learning, Breast Cancer Screening, Saliency Map, Balanced Accuracy, Generative Adversarial Networks, Dense Layer, Cancer Samples, False Negative Rate, Domain Adaptation, Global Average Pooling, Custom Model, Breast cancer, invasive ductal carcinoma, EfficientNetV2-B3

Disciplines

Biotechnology | Business | Management Information Systems | Oncology

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 ( 50×50 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 under sampled 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.

DOI

10.1109/ACCESS.2025.3574210

Version

Publisher's PDF

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Volume

13

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