Improving Digital Image Quality for Convolution Neural Network Based Computer-Aided Diagnosis (CNN-CAD) of Skin Cancer

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dc.contributor.authorYalçın, Tolga
dc.contributor.authorGürsel, Amira Tandirovic
dc.date.accessioned2025-01-06T17:24:29Z
dc.date.available2025-01-06T17:24:29Z
dc.date.issued2021
dc.departmentAdana Alparslan Türkeş Bilim ve Teknoloji Üniversitesi
dc.description.abstractThe practice of detecting skin cancer is based primarily on a visual examination by a dermatologist,\rfollowed by a series of tests for a more accurate diagnosis. The concept “the earlier cancer is detected in\rits natural history, the more effective the treatment is likely to be\" is also valid for skin cancer. Hence,\rany delayed or missed diagnosis can lead to a more severe clinical stage or, what's worse, death. On the\rother hand, the lack of biomarkers in clinical use brings about overdiagnosis and unnecessary biopsies.\rDL-CAD system seems to be an excellent candidate for improving diagnostic accuracy and reducing\runnecessary treatments. However, the vast majority of conventional CADs manipulate dermoscopic\rimages, which require not only costly equipment but also time-consuming processing. Despite the\rdifficulties with precision, state-of-the-art DL-CAD systems provide an interpretation using digital\rimages, requiring no expertise in cost-effective dermoscopic image capture and interpretation. Preprocessing\rmethods play a crucial role in solving this problem. This study presents results with regard to\rpre-processing steps to improve the images to be used in the diagnosis of the 5 most common skin cancer\rtypes for the proposed CNN based ResNet50 deep learning model. To the best of our knowledge it is the\rfirst time that ResNet50 deep-learning model has been utilized in diagnosis of skin cancer.
dc.identifier.endpage1110
dc.identifier.issn2757-9255
dc.identifier.issue4
dc.identifier.startpage1099
dc.identifier.trdizinid509317
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/509317
dc.identifier.urihttps://hdl.handle.net/20.500.14669/1274
dc.identifier.volume36
dc.indekslendigikaynakTR-Dizin
dc.language.isotr
dc.relation.ispartofÇukurova Üniversitesi Mühendislik Fakültesi dergisi
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241211
dc.titleImproving Digital Image Quality for Convolution Neural Network Based Computer-Aided Diagnosis (CNN-CAD) of Skin Cancer
dc.typeArticle

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