dc.contributor.author |
Dahanayake, Yeheni |
|
dc.date.accessioned |
2024-03-29T07:16:16Z |
|
dc.date.available |
2024-03-29T07:16:16Z |
|
dc.date.issued |
2023 |
|
dc.identifier.citation |
Dahanayake, Yeheni (2023) Nail Disease Identification and Treatment Recommendation System. BSc. Dissertation, Informatics Institute of Technology |
en_US |
dc.identifier.issn |
20191054 |
|
dc.identifier.uri |
http://dlib.iit.ac.lk/xmlui/handle/123456789/1948 |
|
dc.description.abstract |
"Nail fungus detection using image processing is a cutting-edge method for determining whether there is nail fungus present. Millions of people worldwide suffer from the widespread condition of nail fungus, which can be extremely uncomfortable and raise aesthetic issues. Visual inspection is the conventional way for identifying nail fungus, however this is frequently subjective and unreliable.
The author has used image processing methods, notably Convolutional Neural Networks (CNNs) coupled with a residual architecture, to get around this restriction. To achieve this, the author performed picture augmentations to construct a dataset." |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
|
en_US |
dc.subject |
Image Processing |
en_US |
dc.subject |
Nail Fungus Detection |
en_US |
dc.subject |
Convolutional Neural Networks |
en_US |
dc.title |
Nail Disease Identification and Treatment Recommendation System |
en_US |
dc.type |
Thesis |
en_US |