Skin Diseases Classification Using Machine Learning and Deep Learning

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dc.contributor.advisorRashedur M. Rahman
dc.contributor.authorMohammad Ashraful Haque Abir
dc.contributor.authorShazid Hasan Riam
dc.contributor.authorMohammed Ariful Karim
dc.contributor.authorAzizul Hakim Tareq
dc.contributor.authorGolam Kibria Anik
dc.contributor.id1520679042
dc.contributor.id1621060042
dc.contributor.id1711153042
dc.contributor.id1711080042
dc.contributor.id1712345642
dc.coverage.departmentElectrical and Computer Engineering
dc.date.accessioned2025-08-13
dc.date.accessioned2025-08-13T05:00:36Z
dc.date.available2025-08-13T05:00:36Z
dc.date.issued2021
dc.description.abstractAccording to the Global Burden of Disease project, skin diseases are the fourth leading cause of benign sickness. Diagnosis of dermatological diseases presents a challenge alongside the absence of trained dermatologists and access to formal medical care. This presents a critical challenge, especially in countries with a large rural population and minimal development. This report aims to study machine learning-based classifiers for predicting skin infections for three classes from a clinical dataset. Convolutional neural network (CNN) has been proved to perform well in image classification. The performance of the neural network is compared with a benchmark multiclass SVM classifier. Additionally, an android based mobile application was implemented, which integrates the classification architecture into the application. The results analysis and possible future works are also discussed in this paper.
dc.description.degreeUndergraduate
dc.identifier.cd600000625
dc.identifier.print-thesisTo be assigned
dc.identifier.urihttps://repository.northsouth.edu/handle/123456789/1363
dc.language.isoen
dc.publisherNorth South University
dc.rights© NSU Library
dc.titleSkin Diseases Classification Using Machine Learning and Deep Learning
dc.typeThesis
oaire.citation.endPage41
oaire.citation.startPage1
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