Jaya Ant lion optimization-driven Deep recurrent neural network for cancer classification using gene expression data.
Cancer is one of the deadly diseases prevailing worldwide and the patients with cancer are rescued only when the cancer is detected at the very early stage. Early detection of cancer is essential as, in the final stage, the chance of survival is limited. The symptoms of cancers are rigorous and ther...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 59; no. 5; pp. 1005 - 1022 |
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| Autores principales: | , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
May2021
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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=150260076&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 150260076 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: May2021 vid: 59 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 150260076 149771170 150260076 NLM33851321 10.1007/s11517-021-02350-w NLM33851321 150260076 ppf: 1005 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Jaya Ant lion optimization-driven Deep recurrent neural network for cancer classification using gene expression data. aug: au: Majji, Ramachandro Nalinipriya, G. Vidyadhari, Ch. Cristin, R. affil: Department of Computer Science and Engineering, GMR Institute of Technology, GMR Nagar, 532127, Razam, Andhra Pradesh, India sug: subj: Neoplasms Classification Recurrent Neural Networks Gene Expression Algorithms Resource Databases ab: Cancer is one of the deadly diseases prevailing worldwide and the patients with cancer are rescued only when the cancer is detected at the very early stage. Early detection of cancer is essential as, in the final stage, the chance of survival is limited. The symptoms of cancers are rigorous and therefore, all the symptoms should be studied properly before the diagnosis. Thus, an automatic prediction system is necessary for classifying cancer as malignant or benign. Hence, this paper introduces the novel strategy based on the JayaAnt lion optimization-based Deep recurrent neural network (JayaALO-based DeepRNN) for cancer classification. The steps followed in the developed model are data normalization, data transformation, feature dimension detection, and classification. The first step is data normalization. The goal of data normalization is to eliminate data redundancy and to mitigate the storage of objects in a relational database that maintains the same information in several places. After that, the data transformation is carried out based on log transformation that generates the patterns using more interpretable and helps fulfill the supposition, and to reduce skew. Also, the non-negative matrix factorization is employed for reducing the feature dimension. Finally, the proposed JayaALO-based DeepRNN method effectively classifies cancer based on the reduced dimension features to produce a satisfactory result. Thus, the resulted output of the proposed JayaALO-based DeepRNN is employed for cancer classification. The proposed JayaALO-based DeepRNN showed improved results with maximal accuracy of 95.97%, maximal sensitivity of 95.95%, and maximal specificity of 96.96%. The goal of this research is to devise the cancer classification strategy using the proposed JayaALO-based DeepRNN. It is required to detect the cancer at an early stage to prevent the destruction caused to the other organs. The developed model involves four phases to perform the cancer classification, namely data normalization, data transformation, feature dimension detection, and the classification. Initially, the input images are gathered and are adapted to perform data normalization. The normalized data is fed to the data transformation, which will be performed using log transformation. The obtained transformed data is fed to feature dimension reduction which is performed using non-negative matrix factorization. The reduced features will be employed in DeepRNN for cancer classification. The training of DeepRNN is done using the proposed JayaALO, which is designed by combining ALO and the Jaya algorithm the block diagram of the proposed cancer classification approach using JayaALO-based DeepRNN approach is given below. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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