LIVER CANCER DETECTION USING HYBRIDIZED FULLY CONVOLUTIONAL NEURAL NETWORK BASED ON DEEP LEARNING FRAMEWORK.
Liver cancer is one in every of the foremost death factors within the world. Early detection and correct staging of liver disease is a crucial issue in sensible radiology. Liver lesions check with those abnormal tissues that are found within the liver. Liver lesions are a wound or injury within the...
| Publicado en: | Turkish Journal of Physiotherapy Rehabilitation Vol. 32; no. 2; pp. 2047 - 2054 |
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| Autores principales: | , , , |
| Formato: | diagnostic images pictorial 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=151006197&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151006197 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: 151006197 151006197 151006197 151006197 ppf: 2047 ppct: 7 formats: fmt: @attributes: type: P tig: atl: LIVER CANCER DETECTION USING HYBRIDIZED FULLY CONVOLUTIONAL NEURAL NETWORK BASED ON DEEP LEARNING FRAMEWORK. aug: au: SHANMUGAVADIVEL, G. KUMAR, PNANDHA KUMAR, V. SNAVEEN PARTHIBAN, V. affil: Assistant Professor, Department of Electronics and Communication Engineering, M. Kumarasamy College of Engineering, Karur, Tamilnadu sug: subj: Liver Neoplasms Diagnosis Neural Networks (Computer) Liver Neoplasms Radiography Deep Learning Conceptual Framework Tomography, X-Ray Computed Machine Learning Image Processing, Computer Assisted ab: Liver cancer is one in every of the foremost death factors within the world. Early detection and correct staging of liver disease is a crucial issue in sensible radiology. Liver lesions check with those abnormal tissues that are found within the liver. Liver lesions are a wound or injury within the tissue areas of the body thanks to injury caused by a wound or sickness. These lesions are often known during a CT scan by a distinction in element intensity from different regions of the liver. For correct clinical treatment, physical segmentation of CT scan is tough and a long task. Instead, computerized segmentation may be a terribly difficult task, as well as liver extend by a hundred and fifty slices of a CT image. Segmentation of liver tumors is a crucial necessity task before any surgical intervention. An exact and correct analysis of the lesions allows for the obtainable therapies that may be provided to the patient. It will facilitate decide the simplest treatment approach also as track the progress of the medical aid over associate interval of your time. So as to subsume this challenge, during this project we have a tendency to propose the utilization of advanced machine learning and segmentation techniques named as guided Active Contour(GAC) technique to cluster the lesions. Then using features extraction techniques to extract the options and phase the liver supported similar pixels. And additionally implement Convolutional Neural Network algorithmic rule to classify the liver diseases with improved accuracy rate. pubtype: Academic Journal doctype: diagnostic images pictorial tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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