Abstract
Oral squamous cell carcinoma (OSCC) diagnosis through computer vision approach is newly introduced technique in the modern diagnostic era. Mitotic cell count from related tissue histopathological images signifies the proliferative marker of cancer cell has been recognized as an essential phenomenon in diagnosis. This paper aims at developing an automated technique for accomplishing the task of mitotic cell count from related histopathological images. In this regard, a new machine learning based methodology incorporating random forest tree classifier learns over four entropy measures, fractal dimension, and seven Hu’s moments based descriptors have been introduced. The performance validation summarizes that proposed methodology can detect mitotic cell efficiently from histopathological images of OSCC with 89% precision, 95% recall or sensitivity, 97.35% specificity, 96.92% accuracy, 96.45% AUC and 92% F-score measure.
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Acknowledgements
The D.K.D. would like to acknowledge Council of Scientific and Industrial Research (CSIR), India for providing financial support to carry out this research under CSIR-SRF scheme (09/81(1203)/2013/EMR-I date. 16.03.2013). C.C., P.M., S.C., A.K.M. and S.B. acknowledge the support provided by Ministry of Human Resource Development, Govt. of India under the Grant Ref. No. 4-23/2014 T.S.I. Date: 14-02-2014. Authors are thankful to the reviewers for their valuable suggestions to improve the quality of this work.
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Das, D.K., Mitra, P., Chakraborty, C. et al. Computational approach for mitotic cell detection and its application in oral squamous cell carcinoma. Multidim Syst Sign Process 28, 1031–1050 (2017). https://doi.org/10.1007/s11045-017-0488-6
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DOI: https://doi.org/10.1007/s11045-017-0488-6