HISTOPATHOLOGICAL IMAGE ANALYSIS FOR ORAL CANCER CLASSIFICATION BY SUPPORT VECTOR MACHINE
DOI:
https://doi.org/10.29284/ijasis.7.2.2021.1-10Keywords:
Oral cancer, histopathological images, medical image analysis, support vector machine, nearest neighbour classifier, supervised classification.Abstract
Oral cancer is caused by the mutation of the cells in the lips or in the mouth. The incidence rate and prevalence rate of oral cancer are increasing worldwide. Recently, the Machine Learning (ML) approaches play a vital role in medical image diagnosis. They provide accurate and rapid evaluation of the analysis of histopathological images using supervised learning. In this study, three different modules are developed namely preprocessing, feature extraction and classification module. Initially, the raw histopathological image is given to the median filter for the removal of background noise in the preprocessing module. In the next module, the temporal features such as energy, entropy etc., are extracted from the color components of the filtered images. Finally, the classification is done by employing the Support Vector Machine (SVM) and K-Nearest Neighbour (KNN) to classify histopathological images as normal or abnormal. Results show that the SVM classifier is better than KNN for the classification of oral cancer. The classification accuracy on 1224 histopathological images has been improved to 98% by using SVM classifier as compared with the KNN results of 83%.
Downloads
References
H. Rajaguru and S. Kumar Prabhakar, "An approach to classification of oral cancer using Softmax Discriminant Classifier," 2nd International Conference on Communication and Electronics Systems, 2017, pp. 420-423.
X. Zhang, W. Liu, M. Dundar, S. Badve and S. Zhang, "Towards Large-Scale Histopathological Image Analysis: Hashing-Based Image Retrieval," IEEE Transactions on Medical Imaging, vol. 34, no. 2, 2015, pp. 496-506.
T. H. Vu, H. S. Mousavi, V. Monga, G. Rao and U. K. A. Rao, "Histopathological Image Classification Using Discriminative Feature-Oriented Dictionary Learning," IEEE Transactions on Medical Imaging, vol. 35, no. 3, 2016, pp. 738-751.
G. Olgun, C. Sokmensuer and C. Gunduz-Demir, "Graph walks for classification of histopathological images," 10th International Symposium on Biomedical Imaging, 2013, pp. 1126-1129.
H. Rajaguru and S. Kumar Prabhakar, "Oral cancer classification from hybrid ABC-PSO and Bayesian LDA," 2nd International Conference on Communication and Electronics Systems, 2017, pp. 230-233.
A. Pandey and N. K. Gupta, "Stage determination of oral cancer using neurofuzzy inference system," IEEE Students' Conference on Electrical, Electronics and Computer Science, 2014, pp. 1-5.
Woonggyu Jung et al., "Advances in oral cancer detection using optical coherence tomography," IEEE Journal of Selected Topics in Quantum Electronics, Vol. 11, No. 4, pp. 811-817, 2005.
N. B.R., G. Kiran A., D. M.S., M. S. and C. H.S., "Texture Analysis of Color Oral Images for Lesion Detection," International Conference on Computational Performance Evaluation, 2020, pp. 067-072.
A. Nawandhar, N. Kumar and L. Yamujala, "Performance Analysis of Neighborhood Component Feature Selection for Oral Histopathology Images," PhD Colloquium on Ethically Driven Innovation and Technology for Society, 2019, pp. 1-8.
Z. Hu, A. Alsadoon, P. Manoranjan, P. W. C. Prasad, S. Ali and A. Elchouemic, "Early stage oral cavity cancer detection: Anisotropic pre-processing and fuzzy C-means segmentation," 8th Annual Computing and Communication Workshop and Conference, 2018, pp. 714-719.
Y. Hu, Z. Zhao and T. Jiang, "Classification of Squamous Cell Carcinoma of the Oral Cavity Using Wavelet Analysis and BP-Chaos Networks," First International Conference on Information Science and Engineering, 2009, pp. 3613-3616.
K. P. Exarchos, Y. Goletsis and D. I. Fotiadis, "Multiparametric Decision Support System for the Prediction of Oral Cancer Reoccurrence," IEEE Transactions on Information Technology in Biomedicine, Vol. 16, No. 6, pp. 1127-1134, 2012.
S. Panigrahi, J. Das, and T. Swarnkar, "Capsule network based analysis of histopathological images of oral squamous cell carcinoma," Journal of King Saud University-Computer and Information Sciences. 2020, pp.1-8.
D. Kadaru and B. Brahmeswara, "Automated classification of Oral Squamous cell carcinoma stages detection using Deep Learning Techniques," European Journal of Molecular & Clinical Medicine, Vol. 7, No. 4, 2020, pp.1111-1119.
I. Amin, H. Zamir, and F.F. Khan, “Histopathological Image Analysis for Oral Squamous Cell Carcinoma classification using concatenated deep learning models," medRxiv, 2021, pp.1-10.
J. Musulin, D. Stifanic, A. Zulijani, T. Cabov, A. Dekanic, and Z. Car, “An enhanced histopathology analysis: An AI-based system for multiclass grading of oral squamous cell carcinoma and segmenting of epithelial and stromal tissue," Cancers, Vol. 13, No. 8, 2021, pp. 1784-2005.
T.Y Rahman, L.B. Mahanta, A.K. Das and J.D. Sarma, "Histopathological imaging database for oral cancer analysis,", Data in brief, Vol. 29, 2020. pp.1-18.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
