Recognition Of Septicemia In Chest X-Ray Images Using Neural networks.
Pneumoniaisoneamongthesicknessesthatpeoplemay experiencein anytime of their lives. Roughly 18% of irresistible infections are brought about by pneumonia. Thisinfectionmay endin deathinsidetheaccompanyingstages. Toanalysepneumoniaas an ailment, lungX-beam pictures areregularlyinspectedby theareaspeci...
| Publicado en: | Turkish Journal of Physiotherapy Rehabilitation Vol. 32; no. 2; pp. 2241 - 2247 |
|---|---|
| Autores principales: | , , |
| Formato: | diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
Turkish Journal of Physiotherapy & Rehabilitation
2021
|
| 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=151006228&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151006228 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: 151006228 151006228 151006228 151006228 ppf: 2241 ppct: 6 formats: fmt: @attributes: type: P tig: atl: Recognition Of Septicemia In Chest X-Ray Images Using Neural networks. aug: au: JOTHIMANI, S. KAVIPRIYA, R. PRAVEENA, K. affil: Assistantprofessor, Department of Electronics and Communication Engineering, Kumarasamy College of Engineering, Karur, Tamil Nadu, India sug: subj: Sepsis Diagnosis Radiography, Thoracic Equipment and Supplies Thorax Pathology Neural Networks (Computer) Human Radiography, Thoracic Methods COVID-19 Algorithms Pneumonia Decision Trees Image Processing, Computer Assisted X-Ray Film Software ab: Pneumoniaisoneamongthesicknessesthatpeoplemay experiencein anytime of their lives. Roughly 18% of irresistible infections are brought about by pneumonia. Thisinfectionmay endin deathinsidetheaccompanyingstages. Toanalysepneumoniaas an ailment, lungX-beam pictures areregularlyinspectedby theareaspecialistsinsidethe clinical practice. In this investigation, lung X-beam pictures that are accessible for theanalysis of pneumonia were utilized. At that point, the measure of profound highlights was diminished from 1000 to100 by utilizing thebase excess greatest significance calculation for each profoundmodel. As needsbe, we accomplished 100 profound highlights from every profound model, and that weconsolidatedthesehighlightssogiveaproficientlistof capabilitiescomprisingof absolutely 300profoundhighlights. In thisprogressionofthe trial, this list of capabilities was given as a contribution to the decision tree, kclosestneighbours, directdiscriminantinvestigation, rectilinearrelapse, andbackingvectorAImodels. Atlast, allmo delsguaranteedpromisingoutcomes;particularlydirectdiscriminantinvestigationyieldedthefirsteffectiveoutcome swithanexactnessof99.41%. We can extend our proposed work to implement the detection of viral infectionsand various classification algorithms used to detect covid-19 and other viral infections. Ourresultwillhelpphysiciantodiagnosisthe viralinfectionofaffectedpeople. pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|