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Consequently, fuzzy extreme learning machine (FELM) is used to classify the healthy, benign, low squamous intraepithelial lesions (LSIL) and high squamous intraepithelial lesions (HSIL). The proposed model acquires the best classification accuracy on Herlev and SIPaKMeD datasets range of 99.6% and 98.5% respectively. As a result, the classification using FELM produces more efficient and accurate result which is significantly high compared to the traditional classifiers. The proposed EN-FELM improves the overall accuracy of 0.2%, 0.13% and 14.6% better than Autoencoder, LSTM and KNN with CNN respectively.<\/jats:p>","DOI":"10.3233\/jifs-220296","type":"journal-article","created":{"date-parts":[[2022,6,7]],"date-time":"2022-06-07T12:37:22Z","timestamp":1654605442000},"page":"6333-6342","source":"Crossref","is-referenced-by-count":7,"title":["Cervical cancer classification using efficient net and fuzzy extreme learning machine"],"prefix":"10.1177","volume":"43","author":[{"given":"A.","family":"Suphalakshmi","sequence":"first","affiliation":[{"name":"Department of AI&DS, Sri Shanmugha College of Engineering and Technology, Sankagiri, Salem"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A.","family":"Ahilan","sequence":"additional","affiliation":[{"name":"Department of ECE, PSN College of Engineering and Technology, Tirunelveli, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A.","family":"Jeyam","sequence":"additional","affiliation":[{"name":"Nuclear Power Corporation of India Limited, Kudankulam, PO, Radhapuram, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Malliga","family":"Subramanian","sequence":"additional","affiliation":[{"name":"Department of CSE, Kongu Engineering College, Perundurai, Erode, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","reference":[{"issue":"20","key":"10.3233\/JIFS-220296_ref1","doi-asserted-by":"crossref","first-page":"30399","DOI":"10.1007\/s11042-020-09607-w","article-title":"DCAVN: Cervical cancer prediction and classification using deep convolutional and variational autoencoder network","volume":"80","author":"Khamparia","year":"2021","journal-title":"Multimedia Tools and Applications"},{"key":"10.3233\/JIFS-220296_ref2","doi-asserted-by":"crossref","first-page":"107791","DOI":"10.1016\/j.asoc.2021.107791","article-title":"Quantitative detection of cervical cancer based on time series information from smear images","volume":"112","author":"Zhang","year":"2021","journal-title":"Applied Soft Computing"},{"key":"10.3233\/JIFS-220296_ref3","unstructured":"Desiani A. , Suprihatin B. , Yahdin S. , Putri A.I. and Husein F.R. , Bi-path Architecture of CNN Segmentation and Classification Method for Cervical Cancer Disorders Based on Pap-smear Images, IAENG International Journal of Computer Science 48(3) (2021)."},{"issue":"5","key":"10.3233\/JIFS-220296_ref4","doi-asserted-by":"crossref","first-page":"3585","DOI":"10.3390\/curroncol28050307","article-title":"Detection of cervical cancer cells in whole slide images using deformable and global context aware faster RCNN-FPN","volume":"28","author":"Li","year":"2021","journal-title":"Current Oncology"},{"issue":"4","key":"10.3233\/JIFS-220296_ref5","first-page":"2470","article-title":"Classification of cervical cancer in women using convolutional neural network","volume":"11","author":"Ramkumar","year":"2021","journal-title":"ICTACT Journal on Image and Video Processing"},{"key":"10.3233\/JIFS-220296_ref6","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.future.2019.12.033","article-title":"Machine learning for assisting cervical cancer diagnosis: An ensemble approach","volume":"106","author":"Lu","year":"2020","journal-title":"Future Generation Computer Systems"},{"issue":"9","key":"10.3233\/JIFS-220296_ref7","doi-asserted-by":"crossref","first-page":"4091","DOI":"10.3390\/app11094091","article-title":"A hierarchical feature-based methodology to perform cervical cancer classification","volume":"11","author":"Diniz","year":"2021","journal-title":"Applied Sciences"},{"issue":"1","key":"10.3233\/JIFS-220296_ref8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-021-93783-8","article-title":"A fuzzy rank-based ensemble of CNN models for classification of cervical cytology","volume":"11","author":"Manna","year":"2021","journal-title":"Scientific Reports"},{"key":"10.3233\/JIFS-220296_ref9","doi-asserted-by":"crossref","unstructured":"Arora A. , Tripathi A. and Bhan A. , Classification of cervical cancer detection using machine learning algorithms. 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