CLASSIFICATION OF STANDARD ORAL CANCER USING TEXTURAL ANALYSIS AND HYBRID HOPFIELD NEURAL NETWORKS.
Oral cancer is a chief health issues in the United States and worldwide. The oral cancer cell detection and segmentation stages are greatly influenced by the intensity distribution, contrast, and clarity of the input phase contrast micrographs. The classification stage in turn is dependent on the se...
| Publicado en: | Turkish Journal of Physiotherapy Rehabilitation Vol. 32; no. 2; pp. 2811 - 2820 |
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| Autores principales: | , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Turkish Journal of Physiotherapy & Rehabilitation
2021
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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=151006299&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151006299 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13008757 YU1 jtl: Turkish Journal of Physiotherapy Rehabilitation issn: 13008757 maglogo: N pubinfo: dt: 2021 vid: 32 iid: 2 pid: 20392 pub: Turkish Journal of Physiotherapy & Rehabilitation place: Kizilay/ Ankara, <Blank> artinfo: ui: 151006299 151006299 151006299 151006299 ppf: 2811 ppct: 9 formats: fmt: @attributes: type: P tig: atl: CLASSIFICATION OF STANDARD ORAL CANCER USING TEXTURAL ANALYSIS AND HYBRID HOPFIELD NEURAL NETWORKS. aug: au: SUJATHA, M. BINDU, K. V. NAGESWARI, D. GEETHAMAHALAKSHMI, G. JAYASANKAR, T. affil: Professor, Department of Electronics and Communication Engineering, KoneruLakshmaiah Education Foundation, Vijayawada, Andrapradesh sug: subj: Mouth Neoplasms Classification Mouth Neoplasms Diagnosis Neural Networks (Computer) Neoplasm Staging Algorithms Human Experimental Studies Sensitivity and Specificity Descriptive Statistics Data Analysis, Statistical ab: Oral cancer is a chief health issues in the United States and worldwide. The oral cancer cell detection and segmentation stages are greatly influenced by the intensity distribution, contrast, and clarity of the input phase contrast micrographs. The classification stage in turn is dependent on the segmentation output. In this research, we used histopathology PAIP 2020 dataset for experimentation. Initially the given dataset taken into pre-processing to remove the noise from the image and enhance the image. Then the pre-processed image is given to the segmentation process, in this processes we used Patch-based Fuzzy Local Similarity CMeans (PFLSCM) scheme. And also we applied feature extraction methods for extract the feature from the image. Total 30 features are extracted, which consists of a combination of size, shape, and first-order and second-order statistical texture measures, were computed. Finally the extracted features images is correctly classify by using Hybrid Hopfield Neural Network with Ant Colony Optimization (ACO) algorithm. The performance of the model is analysed by using different parametric metrics, which are followed in result section. Finally the proposed model achieved the accuracy of 98.98% of accuracy. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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