Dense Convolutional Neural Network for Detection of Cancer from CT Images.
In this paper, we develop a detection module with strong training testing to develop a dense convolutional neural network model. The model is designed in such a way that it is trained with necessary features for optimal modelling of the cancer detection. The method involves preprocessing of computer...
| Publicado en: | BioMed Research International pp. 1 - 9 |
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| Autores principales: | , , , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
6/20/2022
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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=157548852&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157548852 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 6/20/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 157548852 157548852 157548852 10.1155/2022/1293548 157548852 ppf: 1 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Dense Convolutional Neural Network for Detection of Cancer from CT Images. aug: au: Sreenivasu, S. V. N. Gomathi, S. Kumar, M. Jogendra Prathap, Lavanya Madduri, Abhishek Almutairi, Khalid M. A. Alonazi, Wadi B. Kali, D. Jayadhas, S. Arockia affil: Department of Computer Science and Engineering, Narasaraopeta Engineering College, Narasaraopeta, Andhra Pradesh 522601, India sug: subj: Neural Networks (Computer) Neoplasms Diagnosis Tomography, X-Ray Computed Human Reliability Simulations Descriptive Statistics Validation Studies Diagnostic Errors Prevention and Control ab: In this paper, we develop a detection module with strong training testing to develop a dense convolutional neural network model. The model is designed in such a way that it is trained with necessary features for optimal modelling of the cancer detection. The method involves preprocessing of computerized tomography (CT) images for optimal classification at the testing stages. A 10-fold cross-validation is conducted to test the reliability of the model for cancer detection. The experimental validation is conducted in python to validate the effectiveness of the model. The result shows that the model offers robust detection of cancer instances that novel approaches on large image datasets. The simulation result shows that the proposed method provides analyzes with 94% accuracy than other methods. Also, it helps to reduce the detection errors while classifying the cancer instances than other methods the several existing methods. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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