Automated Mitotic Cell Detection and Classification for Breast Cancer Histopathological Images
R. Geetha1, M. Sivajothi2
1R. Geetha, Research Scholar, Department of Computer Science, Manonmaniam Sundaranar University, Tirunelveli (Tamil Nadu), India.
2Dr. M. Sivajothi, Associate Professor, Department of Computer Science, Sri Parasakthi College for Women, Courtallam (Tamil Nadu), India.
Manuscript received on 13 December 2018 | Revised Manuscript received on 22 December 2018 | Manuscript Published on 30 December 2018 | PP: 336-343 | Volume-8 Issue-2S, December 2018 | Retrieval Number: 100.1/ijeat.B10701282S18/18©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Breast cancer tops the list of life-threatening disease with greater mortality rates for women population. However, the mortality rates caused by breast cancer can be minimized by inculcating periodical screening. Histopathological images are utilized by the pathologists for diagnosing or staging the cancerous growth. As the histopathological images are so intricate, it is quite difficult to analyse the images manually. Understanding the involved difficulty, this work presents an automated mitotic cell detection and classification for breast cancer histopathological images. The performance of the proposed approach is analysed in terms of standard performance measures such as accuracy, sensitivity, specificity and time consumption. The performance of the proposed approach outperforms the existing approaches.
Keywords: Histopathological Images, Breast Cancer, Mitotic Cell Detection.
Scope of the Article: Image Security