Publication: Design and Implementation of a Deep Learning-Empowered m-Health Application
dc.contributor.author | AKBULUT, AKHAN | |
dc.contributor.author | DESOUKI, SARA | |
dc.contributor.author | ABDELKHALIQ, SARA | |
dc.contributor.author | KHANTOMANI, LAYAL | |
dc.contributor.author | Çatal, Çağatay | |
dc.date.accessioned | 2023-10-27T08:24:34Z | |
dc.date.available | 2023-10-27T08:24:34Z | |
dc.date.issued | 2023 | |
dc.description.abstract | Many people are unaware of the severity of melanoma disease even though such a disease can be fatal if not treated early. This research aims to facilitate the diagnosis of melanoma disease in people using a mobile health application because some people do not prefer to visit a dermatologist due to several concerns such as feeling uncomfortable by exposing their bodies. As such, a skincare application was developed so that a user can easily analyze a mole at any part of the body and get the diagnosis results quickly. In the first phase, the corresponding image is extracted and sent to a web service. Later, the web service classifies using the pre-trained model built based on a deep learning algorithm. The final phase displays the confidence rates on the mobile application. The proposed model utilizes the Convolutional Neural Network and provides 84% accuracy and 72% precision. The results demonstrate that the proposed model and the corresponding mobile application provide remarkable results for addressing the specified health problem. | en |
dc.description.sponsorship | Qatar National Research Fund (QNRF) | |
dc.identifier.citation | Akbulut, A., Desouki, S., AbdelKhaliq, S. et al. Design and implementation of a deep learning-empowered m-Health application. Multimed Tools Appl (2023). | |
dc.identifier.issn | 1380-7501 | |
dc.identifier.scopus | 2-s2.0-85172917797 | |
dc.identifier.uri | https://doi.org/10.1007/s11042-023-17041-x | |
dc.identifier.uri | https://hdl.handle.net/11413/8849 | |
dc.identifier.wos | 1076639200001 | |
dc.language.iso | en | |
dc.publisher | Springer | |
dc.relation.journal | Multimedia Tools and Applications | |
dc.rights | info:eu-repo/semantics/openAccess | |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | en |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | |
dc.subject | Melanoma | |
dc.subject | Machine Learning | |
dc.subject | Deep Learning | |
dc.subject | m-Health | |
dc.subject | Skin Lesion Analysis | |
dc.title | Design and Implementation of a Deep Learning-Empowered m-Health Application | en |
dc.type | Article Early Access | |
dspace.entity.type | Publication | |
local.indexed.at | WOS | |
local.indexed.at | Scopus | |
local.journal.endpage | 17 | |
local.journal.startpage | 1 | |
relation.isAuthorOfPublication | 6ee0b32b-faed-495d-ac4d-8a263d1ff889 | |
relation.isAuthorOfPublication.latestForDiscovery | 6ee0b32b-faed-495d-ac4d-8a263d1ff889 |