Abstract: Histological grading quantifies the tumor architecture and the cytology deviation of breast cancer against normal tissue. Nottingham Grading System grades the breast cancer classification and allots tumor scores. Mitotic detection is one of the major components in the Nottingham Grading System. Using a conventional microscope is time-consuming, semi-quantitative and has limited histological parameters. Digital scanners scan the tissue slice into high-resolution virtual images called whole slide images. Deep learning models on whole slide images provide a fast and accurate quantitative diagnosis. This paper proposes two deep learning models namely Faster R-CNN and YOLOv5 to detect mitosis on WSI. The proposed Deep Learning models uses 56258 annotated tiles for training/testing and provide F1 score as 84%. The proposed model uses a web-based imaging analysis and diagnosis platform called CADD4MBC for image uploading, Annotation and visualization. This paper proposes an end-to-end web based Deep Learning detection for Breast Cancer Mitosis.

Rajasekaran Subramanian, R. Devika Rubi, Rohit Tapadia, Katakam Karthik, Mohammad Faseeh Ahmed and Allam Manudeep, “Web based Mitosis Detection on Breast Cancer Whole Slide Images using Faster R-CNN and YOLOv5” International Journal of Advanced Computer Science and Applications(IJACSA), 13(12), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0131268

@article{Subramanian2022,
title = {Web based Mitosis Detection on Breast Cancer Whole Slide Images using Faster R-CNN and YOLOv5},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0131268},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0131268},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {12},
author = {Rajasekaran Subramanian and R. Devika Rubi and Rohit Tapadia and Katakam Karthik and Mohammad Faseeh Ahmed and Allam Manudeep}
}