Version 1
: Received: 10 May 2024 / Approved: 10 May 2024 / Online: 10 May 2024 (16:54:38 CEST)
How to cite:
Balasubramanian, A.; Al-Heejawi, S. M. A.; Singh, A.; Breggia, A.; Ahmad, B.; Christman, R.; Stephen T., R.; Amal, S. Ensemble Deep Learning-Based Image Classification for Breast Cancer Subtype and Invasiveness Diagnosis from Whole Slide Image Histopathology. Preprints2024, 2024050711. https://doi.org/10.20944/preprints202405.0711.v1
Balasubramanian, A.; Al-Heejawi, S. M. A.; Singh, A.; Breggia, A.; Ahmad, B.; Christman, R.; Stephen T., R.; Amal, S. Ensemble Deep Learning-Based Image Classification for Breast Cancer Subtype and Invasiveness Diagnosis from Whole Slide Image Histopathology. Preprints 2024, 2024050711. https://doi.org/10.20944/preprints202405.0711.v1
Balasubramanian, A.; Al-Heejawi, S. M. A.; Singh, A.; Breggia, A.; Ahmad, B.; Christman, R.; Stephen T., R.; Amal, S. Ensemble Deep Learning-Based Image Classification for Breast Cancer Subtype and Invasiveness Diagnosis from Whole Slide Image Histopathology. Preprints2024, 2024050711. https://doi.org/10.20944/preprints202405.0711.v1
APA Style
Balasubramanian, A., Al-Heejawi, S. M. A., Singh, A., Breggia, A., Ahmad, B., Christman, R., Stephen T., R., & Amal, S. (2024). Ensemble Deep Learning-Based Image Classification for Breast Cancer Subtype and Invasiveness Diagnosis from Whole Slide Image Histopathology. Preprints. https://doi.org/10.20944/preprints202405.0711.v1
Chicago/Turabian Style
Balasubramanian, A., Ryan Stephen T. and Saeed Amal. 2024 "Ensemble Deep Learning-Based Image Classification for Breast Cancer Subtype and Invasiveness Diagnosis from Whole Slide Image Histopathology" Preprints. https://doi.org/10.20944/preprints202405.0711.v1
Abstract
Cancer diagnosis and classification are pivotal for effective patient management and treatment planning. In this study, we present a comprehensive approach utilizing ensemble deep learning techniques to analyze breast cancer histopathology images. Our datasets are based on two widely employed datasets from different centers for two different tasks: BACH and BreakHis. Within the BACH Dataset, we deployed an ensemble strategy incorporating VGG16 and ResNet50 architec-tures to achieve precise classification of breast cancer histopathology images. Introducing a novel image patching technique, to preprocess a high-resolution image, which facilitates focused analysis of localized regions of interest. The annotated BACH dataset encompasses 400 WSIs across four distinct classes: Normal, Benign, In Situ Carcinoma, and Invasive Carcinoma. In addition, we employed the BreakHis dataset, utilizing VGG16, ResNet34, and ResNet50 models to classify mi-croscopic images into eight distinct categories (four benign and four malignant). For both models we leveraged a five-fold cross-validation approach for rigorous training and testing. Preliminary ex-perimental results indicate a Patch classification accuracy of 95.31% (on BACH dataset) and WSI image classification accuracy of 98.43% (BreakHis). This research significantly contributes to on-going endeavors in harnessing artificial intelligence to advance breast cancer diagnosis, potentially fostering improved patient outcomes and alleviating healthcare burdens.
Keywords
Prostate Cancer Diagnosis; Ensemble Deep Learning; Image Processing; Foundation Models; Computer Vision; Digital Pathology; Artificial Intelligence
Subject
Medicine and Pharmacology, Pathology and Pathobiology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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