MobileSkin: Classification of skin lesion images acquired using mobile phone-attached hand-held dermoscopes

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dc.authoridGülsüm Gençoğlan / 0000-0002-0650-0722
dc.authorscopusidGülsüm Gençoğlan / 56074135100
dc.authorwosidGülsüm Gençoğlan / CQE-8195-2022en_US
dc.contributor.authorYılmaz, Abdurrahim
dc.contributor.authorGençoğlan, Gülsüm
dc.contributor.authorVarol, Rahmetullah
dc.contributor.authorDemirçalı, Ali Anıl
dc.contributor.authorKeshavarz, Meysam
dc.contributor.authorUvet, Hüseyin
dc.date.accessioned2022-09-19T14:10:28Z
dc.date.available2022-09-19T14:10:28Z
dc.date.issued2022en_US
dc.departmentİstinye Üniversitesi, Tıp Fakültesi, Dahili Tıp Bilimleri Bölümüen_US
dc.description.abstractDermoscopy is the visual examination of the skin under a polarized or non-polarized light source. By using dermoscopic equipment, many lesion patterns that are invisible under visible light can be clearly distinguished. Thus, more accurate decisions can be made regarding the treatment of skin lesions. The use of images collected from a dermoscope has both increased the performance of human examiners and allowed the development of deep learning models. The availability of large-scale dermoscopic datasets has allowed the development of deep learning models that can classify skin lesions with high accuracy. However, most dermoscopic datasets contain images that were collected from digital dermoscopic devices, as these devices are frequently used for clinical examination. However, dermatologists also often use non-digital hand-held (optomechanical) dermoscopes. This study presents a dataset consisting of dermoscopic images taken using a mobile phone-attached hand-held dermoscope. Four deep learning models based on the MobileNetV1, MobileNetV2, NASNetMobile, and Xception architectures have been developed to classify eight different lesion types using this dataset. The number of images in the dataset was increased with different data augmentation methods. The models were initialized with weights that were pre-trained on the ImageNet dataset, and then they were further fine-tuned using the presented dataset. The most successful models on the unseen test data, MobileNetV2 and Xception, had performances of 89.18% and 89.64%. The results were evaluated with the 5-fold cross-validation method and compared. Our method allows for automated examination of dermoscopic images taken with mobile phone-attached hand-held dermoscopes.en_US
dc.identifier.citationYilmaz, A., Gencoglan, G., Varol, R., Demircali, A. A., Keshavarz, M., Uvet, H. (2022). MobileSkin: Classification of skin lesion images acquired using mobile phone-attached hand-held dermoscopes. Journal of Clinical Medicine, 11(17).en_US
dc.identifier.doi10.3390/jcm11175102en_US
dc.identifier.issn2077-0383en_US
dc.identifier.issue17en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.3390/jcm11175102
dc.identifier.urihttps://hdl.handle.net/20.500.12713/3162
dc.identifier.volume11en_US
dc.identifier.wosWOS:000851174000001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorGençoğlan, Gülsüm
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.relation.ispartofJOURNAL OF CLINICAL MEDICINEen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDeep Learningen_US
dc.subjectHand-Held Dermoscopeen_US
dc.subjectLightweight Architecturesen_US
dc.subjectMobile Phoneen_US
dc.subjectSkin Canceren_US
dc.titleMobileSkin: Classification of skin lesion images acquired using mobile phone-attached hand-held dermoscopesen_US
dc.typeArticleen_US

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