Hybrid Transfer Learning for Classification of Uterine Cervix Images for Cervical Cancer Screening.
Transfer learning using deep pre-trained convolutional neural networks is increasingly used to solve a large number of problems in the medical field. In spite of being trained using images with entirely different domain, these networks are flexible to adapt to solve a problem in a different domain t...
| Publicado en: | Journal of Digital Imaging Vol. 33; no. 3; pp. 619 - 632 |
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| Autores principales: | , , |
| Formato: | diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=143476522&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 143476522 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2020 vid: 33 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 143476522 143476522 143476522 10.1007/s10278-019-00269-1 143476522 ppf: 619 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Hybrid Transfer Learning for Classification of Uterine Cervix Images for Cervical Cancer Screening. aug: au: Kudva, Vidya Prasad, Keerthana Guruvare, Shyamala affil: Manipal School of Information Sciences, Manipal Academy of Higher Education, 576104, Manipal, India sug: subj: Cervix Neoplasms Diagnosis Cancer Screening Methods Neural Networks (Computer) Machine Learning Sensitivity and Specificity Human Artificial Intelligence Deep Learning Validity ab: Transfer learning using deep pre-trained convolutional neural networks is increasingly used to solve a large number of problems in the medical field. In spite of being trained using images with entirely different domain, these networks are flexible to adapt to solve a problem in a different domain too. Transfer learning involves fine-tuning a pre-trained network with optimal values of hyperparameters such as learning rate, batch size, and number of training epochs. The process of training the network identifies the relevant features for solving a specific problem. Adapting the pre-trained network to solve a different problem requires fine-tuning until relevant features are obtained. This is facilitated through the use of large number of filters present in the convolutional layers of pre-trained network. A very few features out of these features are useful for solving the problem in a different domain, while others are irrelevant, use of which may only reduce the efficacy of the network. However, by minimizing the number of filters required to solve the problem, the efficiency of the training the network can be improved. In this study, we consider identification of relevant filters using the pre-trained networks namely AlexNet and VGG-16 net to detect cervical cancer from cervix images. This paper presents a novel hybrid transfer learning technique, in which a CNN is built and trained from scratch, with initial weights of only those filters which were identified as relevant using AlexNet and VGG-16 net. This study used 2198 cervix images with 1090 belonging to negative class and 1108 to positive class. Our experiment using hybrid transfer learning achieved an accuracy of 91.46%. pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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