Name
Title | ||
|---|---|---|
Computational approach for mitotic cell detection and its application in oral squamous cell carcinoma |
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s11045-017-0488-6 | Multidim. Syst. Sign. Process. |
Keywords | Field | DocType |
Oral squamous cell carcinoma,Mitotic cell,Entropy measure,Fractal dimension,Random forest tree | Oncology,Automated technique,Mathematical optimization,Cancer cell,Internal medicine,Mitotic cell,Cell,Bioinformatics,Classifier (linguistics),Random forest,Mathematics,Carcinoma | Journal |
Volume | Issue | ISSN |
28 | 3 | 0923-6082 |
Citations | PageRank | References |
2 | 0.36 | 15 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Devkumar Das | 1 | 15 | 2.10 |
| Pabitra Mitra | 2 | 1729 | 126.79 |
| Chandan Chakraborty | 3 | 537 | 50.60 |
| Sanjoy Chatterjee | 4 | 6 | 2.67 |
| Asok Kumar Maiti | 5 | 6 | 1.66 |
| Surajit Bose | 6 | 2 | 0.36 |