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http://hdl.handle.net/10603/586024
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DC Field | Value | Language |
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dc.coverage.spatial | ||
dc.date.accessioned | 2024-08-28T11:45:19Z | - |
dc.date.available | 2024-08-28T11:45:19Z | - |
dc.identifier.uri | http://hdl.handle.net/10603/586024 | - |
dc.description.abstract | xv 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.extent | All Pages | |
dc.language | English | |
dc.relation | ||
dc.rights | university | |
dc.title | Implementation of Hybrid Deep Neural Network for Classification of Tuberculosis from XRay Images | |
dc.title.alternative | ||
dc.creator.researcher | Patel, Sneha | |
dc.subject.keyword | Computer Science | |
dc.subject.keyword | Computer Science Artificial Intelligence | |
dc.subject.keyword | Deep Neural Network, Artificial Neural Network, Convolution Neural Network, ENT | |
dc.subject.keyword | Engineering and Technology | |
dc.description.note | ||
dc.contributor.guide | Soni, Nayan | |
dc.publisher.place | Ahmedabad | |
dc.publisher.university | Sabarmati University | |
dc.publisher.institution | Pure and Applied Sciences | |
dc.date.registered | 2019 | |
dc.date.completed | 2023 | |
dc.date.awarded | 2023 | |
dc.format.dimensions | ||
dc.format.accompanyingmaterial | None | |
dc.source.university | University | |
dc.type.degree | Ph.D. | |
Appears in Departments: | Pure & Applied Sciences |
Files in This Item:
File | Description | Size | Format | |
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01_title.pdf | Attached File | 108.36 kB | Adobe PDF | View/Open |
02_prelim pages.pdf | 335.12 kB | Adobe PDF | View/Open | |
03_content.pdf | 108.5 kB | Adobe PDF | View/Open | |
04_abstract.pdf | 31.6 kB | Adobe PDF | View/Open | |
05_chapter 1.pdf | 991.33 kB | Adobe PDF | View/Open | |
06_chapter 2.pdf | 172.08 kB | Adobe PDF | View/Open | |
07_chapter 3.pdf | 224.99 kB | Adobe PDF | View/Open | |
08_chapter 4.pdf | 287.51 kB | Adobe PDF | View/Open | |
09_chapter 5.pdf | 990.94 kB | Adobe PDF | View/Open | |
10_annexure.pdf | 1.36 MB | Adobe PDF | View/Open | |
80_recommendation.pdf | 84.13 kB | Adobe PDF | View/Open | |
reference.pdf | 379.81 kB | Adobe PDF | View/Open |
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