eprintid: 16308 rev_number: 2 eprint_status: archive userid: 1 dir: disk0/00/01/63/08 datestamp: 2023-12-19 03:22:50 lastmod: 2023-12-19 03:22:50 status_changed: 2023-12-19 03:06:01 type: article metadata_visibility: show creators_name: Alquran, H. creators_name: Alsalatie, M. creators_name: Mustafa, W.A. creators_name: Abdi, R.A. creators_name: Ismail, A.R. title: Cervical Net: A Novel Cervical Cancer Classification Using Feature Fusion ispublished: pub note: cited By 17 abstract: Cervical cancer, a common chronic disease, is one of the most prevalent and curable cancers among women. Pap smear images are a popular technique for screening cervical cancer. This study proposes a computer-aided diagnosis for cervical cancer utilizing the novel Cervical Net deep learning (DL) structures and feature fusion with Shuffle Net structural features. Image acquisition and enhancement, feature extraction and selection, as well as classification are the main steps in our cervical cancer screening system. Automated features are extracted using pre-trained convolutional neural networks (CNN) fused with a novel Cervical Net structure in which 544 resultant features are obtained. To minimize dimensionality and select the most important features, principal component analysis (PCA) is used as well as canonical correlation analysis (CCA) to obtain the best discriminant features for five classes of Pap smear images. Here, five different machine learning (ML) algorithms are fed into these features. The proposed strategy achieved the best accuracy ever obtained using a support vector machine (SVM), in which fused features between Cervical Net and Shuffle Net is 99.1 for all classes. © 2022 by the authors. date: 2022 publisher: MDPI official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140460394&doi=10.3390%2fbioengineering9100578&partnerID=40&md5=e6e517aa0e6b9474634b13f318242ab9 id_number: 10.3390/bioengineering9100578 full_text_status: none publication: Bioengineering volume: 9 number: 10 refereed: TRUE issn: 23065354 citation: Alquran, H. and Alsalatie, M. and Mustafa, W.A. and Abdi, R.A. and Ismail, A.R. (2022) Cervical Net: A Novel Cervical Cancer Classification Using Feature Fusion. Bioengineering, 9 (10). ISSN 23065354