Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/586024
Full metadata record
DC FieldValueLanguage
dc.coverage.spatial
dc.date.accessioned2024-08-28T11:45:19Z-
dc.date.available2024-08-28T11:45:19Z-
dc.identifier.urihttp://hdl.handle.net/10603/586024-
dc.description.abstractxv newlineABSTRACT newlineMedical imaging plays a significant role in a variety of clinical applications, such as surgeries, by newlinefacilitating early detection, monitoring, diagnosis, and therapy evaluation of various diseases. newlineAccording to the survey findings, it is recommended that a greater number of patients get X-ray newlineexaminations, hence increasing the frequency of such inspections. The increase in employment newlineopportunities for ENT radiologists has the potential to enhance the likelihood of diagnostic errors. newlineThe process of finding a solution to a problem or equation. Various scholarly publications have newlineidentified a range of challenges and problems pertaining to lung illness and respiratory disease. newlinePneumonia, asthma, TB, fibrosis, and other related conditions. We own a quantity of an algorithm. newlineThe present study focuses on the techniques and methodologies employed in the identification and newlinecategorization of phenomena for the purpose of timely identification, verification, and diagnosis. newlineThe evaluation of therapeutic interventions for various medical conditions. Algorithms such as newlineConvolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), Artificial Neural newlineNetworks (ANNs), and Machine Learning (ML) techniques are commonly employed in various newlinedomains. newlineMachine learning is a subfield of artificial intelligence that focuses on the development of newlinealgorithms and statistical models that enable computer systems to learn and This study focuses on newlinethe investigation and analysis of detection and classification methodologies. Various techniques newlinehave a significant part in the field of Medical Science. Furthermore, I have comprehended newlinealgorithms. To achieve enhanced progress in the current system, it is necessary to conduct a newlinecomprehensive analysis and evaluation. The utilization of Deep neural networks (DNNs) has newlinexvi newlinebecome increasingly prevalent in the field of machine learning using hybrid approach in deep newlinelearning based approach achieve better result for classify Tb images and also work on other dataset. newline
dc.format.extentAll Pages
dc.languageEnglish
dc.relation
dc.rightsuniversity
dc.titleImplementation of Hybrid Deep Neural Network for Classification of Tuberculosis from XRay Images
dc.title.alternative
dc.creator.researcherPatel, Sneha
dc.subject.keywordComputer Science
dc.subject.keywordComputer Science Artificial Intelligence
dc.subject.keywordDeep Neural Network, Artificial Neural Network, Convolution Neural Network, ENT
dc.subject.keywordEngineering and Technology
dc.description.note
dc.contributor.guideSoni, Nayan
dc.publisher.placeAhmedabad
dc.publisher.universitySabarmati University
dc.publisher.institutionPure and Applied Sciences
dc.date.registered2019
dc.date.completed2023
dc.date.awarded2023
dc.format.dimensions
dc.format.accompanyingmaterialNone
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Pure & Applied Sciences

Files in This Item:
File Description SizeFormat 
01_title.pdfAttached File108.36 kBAdobe PDFView/Open
02_prelim pages.pdf335.12 kBAdobe PDFView/Open
03_content.pdf108.5 kBAdobe PDFView/Open
04_abstract.pdf31.6 kBAdobe PDFView/Open
05_chapter 1.pdf991.33 kBAdobe PDFView/Open
06_chapter 2.pdf172.08 kBAdobe PDFView/Open
07_chapter 3.pdf224.99 kBAdobe PDFView/Open
08_chapter 4.pdf287.51 kBAdobe PDFView/Open
09_chapter 5.pdf990.94 kBAdobe PDFView/Open
10_annexure.pdf1.36 MBAdobe PDFView/Open
80_recommendation.pdf84.13 kBAdobe PDFView/Open
reference.pdf379.81 kBAdobe PDFView/Open


Items in Shodhganga are licensed under Creative Commons Licence Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).

Altmetric Badge: