A fuzzy rank-based ensemble of CNN models for classification of cervical cytology

Abstract Cervical cancer affects more than 0.5 million women annually causing more than 0.3 million deaths. Detection of cancer in its early stages is of prime importance for eradicating the disease from the patient’s body. However, regular population-wise screening of cancer is limited by its expen...

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Autores principales:	Ankur Manna, Rohit Kundu, Dmitrii Kaplun, Aleksandr Sinitca, Ram Sarkar
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f0d0083334da4eac973417eb7d2d6fac
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spelling	oai:doaj.org-article:f0d0083334da4eac973417eb7d2d6fac2021-12-02T16:14:16ZA fuzzy rank-based ensemble of CNN models for classification of cervical cytology10.1038/s41598-021-93783-82045-2322https://doaj.org/article/f0d0083334da4eac973417eb7d2d6fac2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93783-8https://doaj.org/toc/2045-2322Abstract Cervical cancer affects more than 0.5 million women annually causing more than 0.3 million deaths. Detection of cancer in its early stages is of prime importance for eradicating the disease from the patient’s body. However, regular population-wise screening of cancer is limited by its expensive and labour intensive detection process, where clinicians need to classify individual cells from a stained slide consisting of more than 100,000 cervical cells, for malignancy detection. Thus, Computer-Aided Diagnosis (CAD) systems are used as a viable alternative for easy and fast detection of cancer. In this paper, we develop such a method where we form an ensemble-based classification model using three Convolutional Neural Network (CNN) architectures, namely Inception v3, Xception and DenseNet-169 pre-trained on ImageNet dataset for Pap stained single cell and whole-slide image classification. The proposed ensemble scheme uses a fuzzy rank-based fusion of classifiers by considering two non-linear functions on the decision scores generated by said base learners. Unlike the simple fusion schemes that exist in the literature, the proposed ensemble technique makes the final predictions on the test samples by taking into consideration the confidence in the predictions of the base classifiers. The proposed model has been evaluated on two publicly available benchmark datasets, namely, the SIPaKMeD Pap Smear dataset and the Mendeley Liquid Based Cytology (LBC) dataset, using a 5-fold cross-validation scheme. On the SIPaKMeD Pap Smear dataset, the proposed framework achieves a classification accuracy of 98.55% and sensitivity of 98.52% in its 2-class setting, and 95.43% accuracy and 98.52% sensitivity in its 5-class setting. On the Mendeley LBC dataset, the accuracy achieved is 99.23% and sensitivity of 99.23%. The results obtained outperform many of the state-of-the-art models, thereby justifying the effectiveness of the same. The relevant codes of this proposed model are publicly available on GitHub .Ankur MannaRohit KunduDmitrii KaplunAleksandr SinitcaRam SarkarNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-18 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ankur Manna Rohit Kundu Dmitrii Kaplun Aleksandr Sinitca Ram Sarkar A fuzzy rank-based ensemble of CNN models for classification of cervical cytology
description	Abstract Cervical cancer affects more than 0.5 million women annually causing more than 0.3 million deaths. Detection of cancer in its early stages is of prime importance for eradicating the disease from the patient’s body. However, regular population-wise screening of cancer is limited by its expensive and labour intensive detection process, where clinicians need to classify individual cells from a stained slide consisting of more than 100,000 cervical cells, for malignancy detection. Thus, Computer-Aided Diagnosis (CAD) systems are used as a viable alternative for easy and fast detection of cancer. In this paper, we develop such a method where we form an ensemble-based classification model using three Convolutional Neural Network (CNN) architectures, namely Inception v3, Xception and DenseNet-169 pre-trained on ImageNet dataset for Pap stained single cell and whole-slide image classification. The proposed ensemble scheme uses a fuzzy rank-based fusion of classifiers by considering two non-linear functions on the decision scores generated by said base learners. Unlike the simple fusion schemes that exist in the literature, the proposed ensemble technique makes the final predictions on the test samples by taking into consideration the confidence in the predictions of the base classifiers. The proposed model has been evaluated on two publicly available benchmark datasets, namely, the SIPaKMeD Pap Smear dataset and the Mendeley Liquid Based Cytology (LBC) dataset, using a 5-fold cross-validation scheme. On the SIPaKMeD Pap Smear dataset, the proposed framework achieves a classification accuracy of 98.55% and sensitivity of 98.52% in its 2-class setting, and 95.43% accuracy and 98.52% sensitivity in its 5-class setting. On the Mendeley LBC dataset, the accuracy achieved is 99.23% and sensitivity of 99.23%. The results obtained outperform many of the state-of-the-art models, thereby justifying the effectiveness of the same. The relevant codes of this proposed model are publicly available on GitHub .
format	article
author	Ankur Manna Rohit Kundu Dmitrii Kaplun Aleksandr Sinitca Ram Sarkar
author_facet	Ankur Manna Rohit Kundu Dmitrii Kaplun Aleksandr Sinitca Ram Sarkar
author_sort	Ankur Manna
title	A fuzzy rank-based ensemble of CNN models for classification of cervical cytology
title_short	A fuzzy rank-based ensemble of CNN models for classification of cervical cytology
title_full	A fuzzy rank-based ensemble of CNN models for classification of cervical cytology
title_fullStr	A fuzzy rank-based ensemble of CNN models for classification of cervical cytology
title_full_unstemmed	A fuzzy rank-based ensemble of CNN models for classification of cervical cytology
title_sort	fuzzy rank-based ensemble of cnn models for classification of cervical cytology
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f0d0083334da4eac973417eb7d2d6fac
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A fuzzy rank-based ensemble of CNN models for classification of cervical cytology

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