A machine learning based framework for assisting pathologists in grading and counting of breast cancer cells

Breast cancer normally occurs in the breast cells of both men and women, but is prominent in women. Computer aided detection increases the chance of early detection and diagnosis. This paper proposes a breast cancer detection method using Nuclear Atypia Scoring (NAS). The proposed cancer detection m...

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Autores principales:	Sreeraj M., Jestin Joy
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Breast cancer NAS scoring Machine learning Deep learning Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/6bc1ccd293a04bb1b5726d71ee40e4b4
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id	oai:doaj.org-article:6bc1ccd293a04bb1b5726d71ee40e4b4
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spelling	oai:doaj.org-article:6bc1ccd293a04bb1b5726d71ee40e4b42021-11-30T04:16:35ZA machine learning based framework for assisting pathologists in grading and counting of breast cancer cells2405-959510.1016/j.icte.2021.02.005https://doaj.org/article/6bc1ccd293a04bb1b5726d71ee40e4b42021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2405959521000217https://doaj.org/toc/2405-9595Breast cancer normally occurs in the breast cells of both men and women, but is prominent in women. Computer aided detection increases the chance of early detection and diagnosis. This paper proposes a breast cancer detection method using Nuclear Atypia Scoring (NAS). The proposed cancer detection method works by converting each and every cancerous tissue into objects. Along with detecting the grade, proposed mechanism gives the count of the detected cells. This assists pathologists in identifying whether cells are cancerous or not along with the count of each type. Proposed model was evaluated on MITOS-ATYPIA-14 Challenge dataset. Accuracy of 0.89 and precision of 0.87 is obtained by the best method. Results indicate that the proposed machine learning technique has better performance as compared to existing methods and can aid pathologists in the detection process.Sreeraj M.Jestin JoyElsevierarticleBreast cancerNAS scoringMachine learningDeep learningInformation technologyT58.5-58.64ENICT Express, Vol 7, Iss 4, Pp 440-444 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Breast cancer NAS scoring Machine learning Deep learning Information technology T58.5-58.64
spellingShingle	Breast cancer NAS scoring Machine learning Deep learning Information technology T58.5-58.64 Sreeraj M. Jestin Joy A machine learning based framework for assisting pathologists in grading and counting of breast cancer cells
description	Breast cancer normally occurs in the breast cells of both men and women, but is prominent in women. Computer aided detection increases the chance of early detection and diagnosis. This paper proposes a breast cancer detection method using Nuclear Atypia Scoring (NAS). The proposed cancer detection method works by converting each and every cancerous tissue into objects. Along with detecting the grade, proposed mechanism gives the count of the detected cells. This assists pathologists in identifying whether cells are cancerous or not along with the count of each type. Proposed model was evaluated on MITOS-ATYPIA-14 Challenge dataset. Accuracy of 0.89 and precision of 0.87 is obtained by the best method. Results indicate that the proposed machine learning technique has better performance as compared to existing methods and can aid pathologists in the detection process.
format	article
author	Sreeraj M. Jestin Joy
author_facet	Sreeraj M. Jestin Joy
author_sort	Sreeraj M.
title	A machine learning based framework for assisting pathologists in grading and counting of breast cancer cells
title_short	A machine learning based framework for assisting pathologists in grading and counting of breast cancer cells
title_full	A machine learning based framework for assisting pathologists in grading and counting of breast cancer cells
title_fullStr	A machine learning based framework for assisting pathologists in grading and counting of breast cancer cells
title_full_unstemmed	A machine learning based framework for assisting pathologists in grading and counting of breast cancer cells
title_sort	machine learning based framework for assisting pathologists in grading and counting of breast cancer cells
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/6bc1ccd293a04bb1b5726d71ee40e4b4
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A machine learning based framework for assisting pathologists in grading and counting of breast cancer cells

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