Enhancing Skin Cancer Diagnosis through the Integration of Deep Learning and Machine Learning Approaches

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dc.contributor.authorDoğan, Yahya
dc.contributor.authorÖzdemir, Cüneyt
dc.date.accessioned2024-12-24T19:16:12Z
dc.date.available2024-12-24T19:16:12Z
dc.date.issued2024
dc.departmentSiirt Üniversitesi
dc.description.abstractSkin cancer is a disease characterized by the uncontrolled proliferation of skin cells, typically manifesting as lesions or abnormal growths. Early diagnosis is critical for improving treatment outcomes. This study proposes an innovative approach to skin cancer diagnosis by integrating modern deep learning models with traditional machine learning algorithms. A three-phase methodology was developed. In the first phase, meaningful features were extracted from skin lesion images using various transfer learning models, including Xception, VGG16, ResNet152V2, InceptionV3, InceptionResNetV2, MobileNetV2, EfficientNetB2, and DenseNet201. In the second phase, dimensionality reduction was performed using Principal Component Analysis (PCA). In the final phase, the reduced feature sets were classified using K-Nearest Neighbors (KNN) and Random Forest (RF) algorithms. Experimental results demonstrated that the highest accuracy of 91.28% was achieved through the combination of DenseNet201 for feature extraction, PCA for dimensionality reduction, and Random Forest for classification. These findings highlight the effectiveness of integrating transfer learning models, dimensionality reduction techniques, and machine learning algorithms in enhancing the accuracy of skin cancer diagnosis.
dc.identifier.doi10.17671/gazibtd.1484037
dc.identifier.endpage347
dc.identifier.issn1307-9697
dc.identifier.issn2147-0715
dc.identifier.issue4
dc.identifier.startpage339
dc.identifier.trdizinid1278425
dc.identifier.urihttps://doi.org/10.17671/gazibtd.1484037
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1278425
dc.identifier.urihttps://hdl.handle.net/20.500.12604/4275
dc.identifier.volume17
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.relation.ispartofBilişim Teknolojileri Dergisi
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241222
dc.subjectMachine learning
dc.subjectfeature selection
dc.subjecttransfer learning
dc.subjectdimensionality reduction
dc.subjectSkin cancer diagnosis
dc.titleEnhancing Skin Cancer Diagnosis through the Integration of Deep Learning and Machine Learning Approaches
dc.typeArticle

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