Computational learning of features for automated colonic polyp classification

Abstract 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 te...

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Autores principales:	Kangkana Bora, M. K. Bhuyan, Kunio Kasugai, Saurav Mallik, Zhongming Zhao
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b9ba95f3c47249c38cb8b7f3f0d80e71
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id	oai:doaj.org-article:b9ba95f3c47249c38cb8b7f3f0d80e71
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spelling	oai:doaj.org-article:b9ba95f3c47249c38cb8b7f3f0d80e712021-12-02T16:23:14ZComputational learning of features for automated colonic polyp classification10.1038/s41598-021-83788-82045-2322https://doaj.org/article/b9ba95f3c47249c38cb8b7f3f0d80e712021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-83788-8https://doaj.org/toc/2045-2322Abstract 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.Kangkana BoraM. K. BhuyanKunio KasugaiSaurav MallikZhongming ZhaoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-16 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kangkana Bora M. K. Bhuyan Kunio Kasugai Saurav Mallik Zhongming Zhao Computational learning of features for automated colonic polyp classification
description	Abstract 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.
format	article
author	Kangkana Bora M. K. Bhuyan Kunio Kasugai Saurav Mallik Zhongming Zhao
author_facet	Kangkana Bora M. K. Bhuyan Kunio Kasugai Saurav Mallik Zhongming Zhao
author_sort	Kangkana Bora
title	Computational learning of features for automated colonic polyp classification
title_short	Computational learning of features for automated colonic polyp classification
title_full	Computational learning of features for automated colonic polyp classification
title_fullStr	Computational learning of features for automated colonic polyp classification
title_full_unstemmed	Computational learning of features for automated colonic polyp classification
title_sort	computational learning of features for automated colonic polyp classification
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b9ba95f3c47249c38cb8b7f3f0d80e71
work_keys_str_mv	AT kangkanabora computationallearningoffeaturesforautomatedcolonicpolypclassification AT mkbhuyan computationallearningoffeaturesforautomatedcolonicpolypclassification AT kuniokasugai computationallearningoffeaturesforautomatedcolonicpolypclassification AT sauravmallik computationallearningoffeaturesforautomatedcolonicpolypclassification AT zhongmingzhao computationallearningoffeaturesforautomatedcolonicpolypclassification
_version_	1718384194589032448

Computational learning of features for automated colonic polyp classification

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