| Autor: |
Bora K; Department of Computer Science and IT, Cotton University, Pan Bazar, Guwahati, Assam, 781001, India., Bhuyan MK; Department of Electrical and Electronics Engineering, Indian Institute of Technology Guwahati (IITG), Guwahati, Assam, 781039, India., Kasugai K; Department of Gastroenterology, Aichi Medical University, Nagakute, 480-1195, Japan., Mallik S; Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA., Zhao Z; Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA. zhongming.zhao@uth.tmc.edu.; Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA. zhongming.zhao@uth.tmc.edu.; Department of Pathology and Laboratory Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA. zhongming.zhao@uth.tmc.edu. |
| Abstrakt: |
Shape, texture, and color are critical features for assessing the degree of dysplasia in colonic polyps. A comprehensive analysis of these features is presented in this paper. Shape features are extracted using generic Fourier descriptor. The nonsubsampled contourlet transform is used as texture and color feature descriptor, with different combinations of filters. Analysis of variance (ANOVA) is applied to measure statistical significance of the contribution of different descriptors between two colonic polyps: non-neoplastic and neoplastic. Final descriptors selected after ANOVA are optimized using the fuzzy entropy-based feature ranking algorithm. Finally, classification is performed using Least Square Support Vector Machine and Multi-layer Perceptron with five-fold cross-validation to avoid overfitting. Evaluation of our analytical approach using two datasets suggested that the feature descriptors could efficiently designate a colonic polyp, which subsequently can help the early detection of colorectal carcinoma. Based on the comparison with four deep learning models, we demonstrate that the proposed approach out-performs the existing feature-based methods of colonic polyp identification. |
