Breast Mass Classification Using Diverse Contextual Information and Convolutional Neural Network

Masses are one of the early signs of breast cancer, and the survival rate of women suffering from breast cancer can be improved if masses can be correctly identified as benign or malignant. However, their classification is challenging due to the similarity in texture patterns of both types of mass....

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Autores principales:	Mariam Busaleh, Muhammad Hussain, Hatim A. Aboalsamh, Fazal-e- Amin
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	breast mass classification mammography transfer learning BIRADS convolutional neural network (CNN) ensemble classifier Biotechnology TP248.13-248.65
Acceso en línea:	https://doaj.org/article/ea482e8921f4486eaf23f5b16f1dce7e
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id	oai:doaj.org-article:ea482e8921f4486eaf23f5b16f1dce7e
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spelling	oai:doaj.org-article:ea482e8921f4486eaf23f5b16f1dce7e2021-11-25T16:54:57ZBreast Mass Classification Using Diverse Contextual Information and Convolutional Neural Network10.3390/bios111104192079-6374https://doaj.org/article/ea482e8921f4486eaf23f5b16f1dce7e2021-10-01T00:00:00Zhttps://www.mdpi.com/2079-6374/11/11/419https://doaj.org/toc/2079-6374Masses are one of the early signs of breast cancer, and the survival rate of women suffering from breast cancer can be improved if masses can be correctly identified as benign or malignant. However, their classification is challenging due to the similarity in texture patterns of both types of mass. The existing methods for this problem have low sensitivity and specificity. Based on the hypothesis that diverse contextual information of a mass region forms a strong indicator for discriminating benign and malignant masses and the idea of the ensemble classifier, we introduce a computer-aided system for this problem. The system uses multiple regions of interest (ROIs) encompassing a mass region for modeling diverse contextual information, a single ResNet-50 model (or its density-specific modification) as a backbone for local decisions, and stacking with SVM as a base model to predict the final decision. A data augmentation technique is introduced for fine-tuning the backbone model. The system was thoroughly evaluated on the benchmark CBIS-DDSM dataset using its provided data split protocol, and it achieved a sensitivity of 98.48% and a specificity of 92.31%. Furthermore, it was found that the system gives higher performance if it is trained and tested using the data from a specific breast density BI-RADS class. The system does not need to fine-tune/train multiple CNN models; it introduces diverse contextual information by multiple ROIs. The comparison shows that the method outperforms the state-of-the-art methods for classifying mass regions into benign and malignant. It will help radiologists reduce their burden and enhance their sensitivity in the prediction of malignant masses.Mariam BusalehMuhammad HussainHatim A. AboalsamhFazal-e- AminMDPI AGarticlebreast mass classificationmammographytransfer learningBIRADSconvolutional neural network (CNN)ensemble classifierBiotechnologyTP248.13-248.65ENBiosensors, Vol 11, Iss 419, p 419 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	breast mass classification mammography transfer learning BIRADS convolutional neural network (CNN) ensemble classifier Biotechnology TP248.13-248.65
spellingShingle	breast mass classification mammography transfer learning BIRADS convolutional neural network (CNN) ensemble classifier Biotechnology TP248.13-248.65 Mariam Busaleh Muhammad Hussain Hatim A. Aboalsamh Fazal-e- Amin Breast Mass Classification Using Diverse Contextual Information and Convolutional Neural Network
description	Masses are one of the early signs of breast cancer, and the survival rate of women suffering from breast cancer can be improved if masses can be correctly identified as benign or malignant. However, their classification is challenging due to the similarity in texture patterns of both types of mass. The existing methods for this problem have low sensitivity and specificity. Based on the hypothesis that diverse contextual information of a mass region forms a strong indicator for discriminating benign and malignant masses and the idea of the ensemble classifier, we introduce a computer-aided system for this problem. The system uses multiple regions of interest (ROIs) encompassing a mass region for modeling diverse contextual information, a single ResNet-50 model (or its density-specific modification) as a backbone for local decisions, and stacking with SVM as a base model to predict the final decision. A data augmentation technique is introduced for fine-tuning the backbone model. The system was thoroughly evaluated on the benchmark CBIS-DDSM dataset using its provided data split protocol, and it achieved a sensitivity of 98.48% and a specificity of 92.31%. Furthermore, it was found that the system gives higher performance if it is trained and tested using the data from a specific breast density BI-RADS class. The system does not need to fine-tune/train multiple CNN models; it introduces diverse contextual information by multiple ROIs. The comparison shows that the method outperforms the state-of-the-art methods for classifying mass regions into benign and malignant. It will help radiologists reduce their burden and enhance their sensitivity in the prediction of malignant masses.
format	article
author	Mariam Busaleh Muhammad Hussain Hatim A. Aboalsamh Fazal-e- Amin
author_facet	Mariam Busaleh Muhammad Hussain Hatim A. Aboalsamh Fazal-e- Amin
author_sort	Mariam Busaleh
title	Breast Mass Classification Using Diverse Contextual Information and Convolutional Neural Network
title_short	Breast Mass Classification Using Diverse Contextual Information and Convolutional Neural Network
title_full	Breast Mass Classification Using Diverse Contextual Information and Convolutional Neural Network
title_fullStr	Breast Mass Classification Using Diverse Contextual Information and Convolutional Neural Network
title_full_unstemmed	Breast Mass Classification Using Diverse Contextual Information and Convolutional Neural Network
title_sort	breast mass classification using diverse contextual information and convolutional neural network
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ea482e8921f4486eaf23f5b16f1dce7e
work_keys_str_mv	AT mariambusaleh breastmassclassificationusingdiversecontextualinformationandconvolutionalneuralnetwork AT muhammadhussain breastmassclassificationusingdiversecontextualinformationandconvolutionalneuralnetwork AT hatimaaboalsamh breastmassclassificationusingdiversecontextualinformationandconvolutionalneuralnetwork AT fazaleamin breastmassclassificationusingdiversecontextualinformationandconvolutionalneuralnetwork
_version_	1718412870333497344

Breast Mass Classification Using Diverse Contextual Information and Convolutional Neural Network

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