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Deep YOLO-Based Detection of Breast Cancer Mitotic-Cells in Histopathological Images

Maisun Mohamed, Al Zorgani,
; 
Irfan, Mehmood,
; 
Hassan,Ugail,
; 
Al Zorgani, Maisun M.
; 
Mehmood, Irfan
; 
Ugail, Hassan
Publication Date
2021-08-15
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Keywords
Deep learning techniques, Mitotic cell counting, YOLO-v2 network, YOLO (You Only Look Once) v2 network, Histopathological image analysis, Histopathological images, Breast cancer histopathology
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Engineering and Digital Technology Publications
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Abstract
Coinciding with advances in whole-slide imaging scanners, it is become essential to automate the conventional image-processing techniques to assist pathologists with some tasks such as mitotic-cells detection. In histopathological images analysing, the mitotic-cells counting is a significant biomarker in the prognosis of the breast cancer grade and its aggressiveness. However, counting task of mitotic-cells is tiresome, tedious and time-consuming due to difficulty distinguishing between mitotic cells and normal cells. To tackle this challenge, several deep learning-based approaches of Computer-Aided Diagnosis (CAD) have been lately advanced to perform counting task of mitotic-cells in the histopathological images. Such CAD systems achieve outstanding performance, hence histopathologists can utilise them as a second-opinion system. However, improvement of CAD systems is an important with the progress of deep learning networks architectures. In this work, we investigate deep YOLO (You Only Look Once) v2 network for mitotic-cells detection on ICPR (International Conference on Pattern Recognition) 2012 dataset of breast cancer histopathology. The obtained results showed that proposed architecture achieves good result of 0.839 F1-measure.
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http://hdl.handle.net/10454/18834
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Citation
Al Zorgani MM, Mehmood I and Ugail H (2022) Deep YOLO-Based Detection of Breast Cancer Mitotic-Cells in Histopathological Images. In: Su R, Zhang YD and Liu H (eds) Proceedings of the 2021 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2021). Lecture Notes in Electrical Engineering. Vol 784 pp 335–342. Singapore, Springer.
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https://doi.org/10.1007/978-981-16-3880-0_35
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Conference paper
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