Web application for monkeypox disease detection using deep learning

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creativework.keywordsVirus ditection, deep learning
dc.contributor.advisorMd. Shahriar Hussain
dc.contributor.authorAHMAD SAMIN SHADMAN
dc.contributor.authorSUMAIYA SHARMEEN
dc.contributor.id1811437042
dc.contributor.id1731500042
dc.coverage.departmentElectrical and Computer Engineering
dc.date.accessioned2024-05-05
dc.date.accessioned2024-05-05T06:09:25Z
dc.date.available2024-05-05T06:09:25Z
dc.date.issued2022
dc.description.abstractThe monkeypox virus might become the next big pandemic, like the COVID-19 pandemic, if it is not monitored and controlled correctly. Monkeypox has some similarities to measles and chickenpox, making it very hard to test for it and give a diagnosis in the early stages. A polymerase chain reaction (PCR) test must be used to test for monkeypox properly. This study aims to detect monkeypox accurately using some popular deep-learning models and then compare the results. We used the “Monkeypox Skin Lesion Dataset (MSLD).” Data augmentation has been done to the data to increase the number of images. A web-based prototype application is to be developed where an image can be uploaded, and a prediction will be given if the image is either monkeypox or not. The model used in the web application is the VGG-16 model which identifies monkeypox images with an accuracy of 99%.
dc.description.degreeUndergraduate
dc.identifier.cd600000036
dc.identifier.print-thesisTo be assigned
dc.identifier.urihttps://repository.northsouth.edu/handle/123456789/578
dc.language.isoen_US
dc.publisherNorth South University
dc.rights© NSU Library
dc.subjectTECHNOLOGY::Electrical engineering, electronics and photonics::Electrical engineering
dc.titleWeb application for monkeypox disease detection using deep learning
dc.typeProject
oaire.citation.endPage34
oaire.citation.startPage1
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