Enhancing the Automated Classification of Dermatological Images by Considering Skin Tone

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dc.contributor.advisorTóth, János
dc.contributor.authorMuyera, Shalom Afwande
dc.contributor.departmentDE--Informatikai Kar
dc.date.accessioned2024-06-23T18:15:04Z
dc.date.available2024-06-23T18:15:04Z
dc.date.created2024-04-22
dc.description.abstractThe field of dermatological image classification has greatly advanced by adopting Machine Learning and Artificial Intelligence. However, it faces some challenges and gaps, such as a lack of diversity in classification models, which leads to misclassification and a lack of efficiency. This research aims at enhancing the correct classification of dermatological images by considering skin tone diversity. A combination of the Fitzpatrick 17k and DDI datasets is used and because of their diversity, transfer learning using EfficientNetB0 is the preferred classification model. Two machine-learning models are created, trained, and tested using images from different skin tones, and the results are evaluated.
dc.description.courseMérnökinformatikus
dc.description.degreeMSc/MA
dc.format.extent49
dc.identifier.urihttps://hdl.handle.net/2437/374584
dc.language.isoen
dc.rights.accessHozzáférhető a 2022 decemberi felsőoktatási törvénymódosítás értelmében.
dc.subjectSkin Tone
dc.subjectDermatological Images
dc.subjectImage Classification
dc.subjectTransfer Learning
dc.subject.dspaceInformatics
dc.titleEnhancing the Automated Classification of Dermatological Images by Considering Skin Tone
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