Multi- class classification of breast cancer abnormalities using Deep Convolutional Neural Network (CNN).

The real cause of breast cancer is very challenging to determine and therefore early detection of the disease is necessary for reducing the death rate due to risks of breast cancer. Early detection of cancer boosts increasing the survival chance up to 8%. Primarily, breast images emanating from mamm...

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Autores principales:	Maleika Heenaye-Mamode Khan, Nazmeen Boodoo-Jahangeer, Wasiimah Dullull, Shaista Nathire, Xiaohong Gao, G R Sinha, Kapil Kumar Nagwanshi
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0c825a16d18940daa17b927531d694c4
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spelling	oai:doaj.org-article:0c825a16d18940daa17b927531d694c42021-12-02T20:19:29ZMulti- class classification of breast cancer abnormalities using Deep Convolutional Neural Network (CNN).1932-620310.1371/journal.pone.0256500https://doaj.org/article/0c825a16d18940daa17b927531d694c42021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0256500https://doaj.org/toc/1932-6203The real cause of breast cancer is very challenging to determine and therefore early detection of the disease is necessary for reducing the death rate due to risks of breast cancer. Early detection of cancer boosts increasing the survival chance up to 8%. Primarily, breast images emanating from mammograms, X-Rays or MRI are analyzed by radiologists to detect abnormalities. However, even experienced radiologists face problems in identifying features like micro-calcifications, lumps and masses, leading to high false positive and high false negative. Recent advancement in image processing and deep learning create some hopes in devising more enhanced applications that can be used for the early detection of breast cancer. In this work, we have developed a Deep Convolutional Neural Network (CNN) to segment and classify the various types of breast abnormalities, such as calcifications, masses, asymmetry and carcinomas, unlike existing research work, which mainly classified the cancer into benign and malignant, leading to improved disease management. Firstly, a transfer learning was carried out on our dataset using the pre-trained model ResNet50. Along similar lines, we have developed an enhanced deep learning model, in which learning rate is considered as one of the most important attributes while training the neural network. The learning rate is set adaptively in our proposed model based on changes in error curves during the learning process involved. The proposed deep learning model has achieved a performance of 88% in the classification of these four types of breast cancer abnormalities such as, masses, calcifications, carcinomas and asymmetry mammograms.Maleika Heenaye-Mamode KhanNazmeen Boodoo-JahangeerWasiimah DullullShaista NathireXiaohong GaoG R SinhaKapil Kumar NagwanshiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0256500 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Maleika Heenaye-Mamode Khan Nazmeen Boodoo-Jahangeer Wasiimah Dullull Shaista Nathire Xiaohong Gao G R Sinha Kapil Kumar Nagwanshi Multi- class classification of breast cancer abnormalities using Deep Convolutional Neural Network (CNN).
description	The real cause of breast cancer is very challenging to determine and therefore early detection of the disease is necessary for reducing the death rate due to risks of breast cancer. Early detection of cancer boosts increasing the survival chance up to 8%. Primarily, breast images emanating from mammograms, X-Rays or MRI are analyzed by radiologists to detect abnormalities. However, even experienced radiologists face problems in identifying features like micro-calcifications, lumps and masses, leading to high false positive and high false negative. Recent advancement in image processing and deep learning create some hopes in devising more enhanced applications that can be used for the early detection of breast cancer. In this work, we have developed a Deep Convolutional Neural Network (CNN) to segment and classify the various types of breast abnormalities, such as calcifications, masses, asymmetry and carcinomas, unlike existing research work, which mainly classified the cancer into benign and malignant, leading to improved disease management. Firstly, a transfer learning was carried out on our dataset using the pre-trained model ResNet50. Along similar lines, we have developed an enhanced deep learning model, in which learning rate is considered as one of the most important attributes while training the neural network. The learning rate is set adaptively in our proposed model based on changes in error curves during the learning process involved. The proposed deep learning model has achieved a performance of 88% in the classification of these four types of breast cancer abnormalities such as, masses, calcifications, carcinomas and asymmetry mammograms.
format	article
author	Maleika Heenaye-Mamode Khan Nazmeen Boodoo-Jahangeer Wasiimah Dullull Shaista Nathire Xiaohong Gao G R Sinha Kapil Kumar Nagwanshi
author_facet	Maleika Heenaye-Mamode Khan Nazmeen Boodoo-Jahangeer Wasiimah Dullull Shaista Nathire Xiaohong Gao G R Sinha Kapil Kumar Nagwanshi
author_sort	Maleika Heenaye-Mamode Khan
title	Multi- class classification of breast cancer abnormalities using Deep Convolutional Neural Network (CNN).
title_short	Multi- class classification of breast cancer abnormalities using Deep Convolutional Neural Network (CNN).
title_full	Multi- class classification of breast cancer abnormalities using Deep Convolutional Neural Network (CNN).
title_fullStr	Multi- class classification of breast cancer abnormalities using Deep Convolutional Neural Network (CNN).
title_full_unstemmed	Multi- class classification of breast cancer abnormalities using Deep Convolutional Neural Network (CNN).
title_sort	multi- class classification of breast cancer abnormalities using deep convolutional neural network (cnn).
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/0c825a16d18940daa17b927531d694c4
work_keys_str_mv	AT maleikaheenayemamodekhan multiclassclassificationofbreastcancerabnormalitiesusingdeepconvolutionalneuralnetworkcnn AT nazmeenboodoojahangeer multiclassclassificationofbreastcancerabnormalitiesusingdeepconvolutionalneuralnetworkcnn AT wasiimahdullull multiclassclassificationofbreastcancerabnormalitiesusingdeepconvolutionalneuralnetworkcnn AT shaistanathire multiclassclassificationofbreastcancerabnormalitiesusingdeepconvolutionalneuralnetworkcnn AT xiaohonggao multiclassclassificationofbreastcancerabnormalitiesusingdeepconvolutionalneuralnetworkcnn AT grsinha multiclassclassificationofbreastcancerabnormalitiesusingdeepconvolutionalneuralnetworkcnn AT kapilkumarnagwanshi multiclassclassificationofbreastcancerabnormalitiesusingdeepconvolutionalneuralnetworkcnn
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Multi- class classification of breast cancer abnormalities using Deep Convolutional Neural Network (CNN).

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