Balasubramanian, A.A.; Al-Heejawi, S.M.A.; Singh, A.; Breggia, A.; Ahmad, B.; Christman, R.; Ryan, S.T.; Amal, S. Ensemble Deep Learning-Based Image Classification for Breast Cancer Subtype and Invasiveness Diagnosis from Whole Slide Image Histopathology. Cancers2024, 16, 2222.
Balasubramanian, A.A.; Al-Heejawi, S.M.A.; Singh, A.; Breggia, A.; Ahmad, B.; Christman, R.; Ryan, S.T.; Amal, S. Ensemble Deep Learning-Based Image Classification for Breast Cancer Subtype and Invasiveness Diagnosis from Whole Slide Image Histopathology. Cancers 2024, 16, 2222.
Balasubramanian, A.A.; Al-Heejawi, S.M.A.; Singh, A.; Breggia, A.; Ahmad, B.; Christman, R.; Ryan, S.T.; Amal, S. Ensemble Deep Learning-Based Image Classification for Breast Cancer Subtype and Invasiveness Diagnosis from Whole Slide Image Histopathology. Cancers2024, 16, 2222.
Balasubramanian, A.A.; Al-Heejawi, S.M.A.; Singh, A.; Breggia, A.; Ahmad, B.; Christman, R.; Ryan, S.T.; Amal, S. Ensemble Deep Learning-Based Image Classification for Breast Cancer Subtype and Invasiveness Diagnosis from Whole Slide Image Histopathology. Cancers 2024, 16, 2222.
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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