Multi-class nucleus detection and classification using deep convolutional neural network with enhanced high dimensional dissimilarity translation model on cervical cells

• Autorzy: Karri Meghana , Annavarapu Chandra Sekhara Rao , Mallik Saurav , Zhao Zhongming , Acharya U. Rajendra

Identyfikatory

DOI

10.1016/j.bbe.2022.06.003

• Języki publikacji: EN

Abstrakty

EN

Advanced cervical screening via liquid-based cytology (LBC)/Pap smear is a highly efficient precancerous cell detection tool based on cell image analysis, in which cells are classified as normal/abnormal. This paper outlines the drawbacks by introducing a new framework for the accurate classification of cervical cells. The proposed methodology comprises three phases: segmentation, localization of nucleus, and classification. In the segmentation phase, we develop a hybrid system that incorporates two binary image patches obtained by a 19-layered convolutional neural network (ConvNet) model with an enhanced deep high dimensional dissimilarity translation (HDDT) based conspicuous segmentation. To get the relevant information from binary patched images, a technique called optimum semantic similarity selective search (OSS-SS) is proposed that returns the localized RGB patched image. A pre-trained ResNet-50 model is retrained using transfer learning on localized patched images in the classification phase. Following that, the selected features from the average pool and fully connected layers are down-sampled using the t-distribution stochastic neighbor embedding (t-SNE) approach. Finally, these combined features are fed into a multi-class weighted kernel extreme learning machine (WKELM) classifier via a sparse multicanonical correlation (SMCCA) method. Three datasets (SIPaKMed, CRIC, and Harlev) are used to evaluate the segmentation and classification task. The proposed approach obtained an accuracy of 99.12 %, specificity of 99.45 %, sensitivity of 99.25 % with an execution time 99.6248 on SIPaKMed. The experimental analysis indicate that our model is more effective than existing techniques.

Słowa kluczowe

EN

cervical cytology; nucleus detection; nucleus classification; pap smear

PL

cytologia szyjki macicy; wykrywanie jądra; klasyfikacja jądra

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2022
• Tom: Vol. 42, no. 3
• Strony: 797--814
• Opis fizyczny: Bibliogr. 71 poz., rys., tab., wykr.

Twórcy

autor

Karri Meghana

• Department of Computer Science and Engineering, Indian Institute of Technology (Indian School of Mines) Dhanbad 826004, Jharkhand, India

autor

Annavarapu Chandra Sekhara Rao

• Department of Computer Science and Engineering, Indian Institute of Technology (Indian School of Mines) Dhanbad, Jharkhand, India

autor

Mallik Saurav

• Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA

autor

Zhao Zhongming

• Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
• Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
• Department of Pathology and Laboratory Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA

autor

Acharya U. Rajendra

• Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, Singapore
• Department of Biomedical Engineering, School of science and Technology, SUSS University, Singapore
• Department of Biomedical Informatics and Medical Engineering, Asia university, Taichung, Taiwan

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