A Comparison of Different Convolutional Neural Network Models for Skin Cancer Diagnosis

Year 2025, Volume: 15 Issue: 1, 25 - 38, 01.03.2025

İbrahim Aruk , Ahmet Nusret Toprak

https://doi.org/10.21597/jist.1575214

Abstract

In recent years, a notable rise in the prevalence of skin cancer has been seen worldwide. Early and correct diagnosis of skin cancer improves treatment success rates and substantially enhances patients' quality of life. Traditional skin cancer diagnostic techniques generally depend on visual evaluations and include a subjective methodology. On the other hand, deep learning algorithms provide effective solutions to improve the accuracy and time of skin cancer diagnostics. This study evaluates the efficacy of advanced Convolutional Neural Network (CNN) models, including EfficientNet, VGG, Inception, DenseNet, and DarkNet, in the classification of skin cancer. Twenty-one CNN models were trained and extensively analyzed on the ISIC 2017 dataset utilizing data augmentation and transfer learning methodologies. The results of the study demonstrated that the EfficientNet-b0 model attained superior performance with an accuracy of 84.00%, precision of 83.63%, sensitivity of 74.96%, and an F1-score of 78.59%. This comprehensive study shows the efficacy of CNN-based models in skin cancer diagnosis and illustrates the promise of these algorithms for future research.

Keywords

Skin cancer classification, Convolutional neural networks, Melanoma

Cilt Kanseri Tanısı için Farklı Evrişimsel Sinir Ağı Modellerinin Karşılaştırılması

Abstract

Son yıllarda, dünya genelinde cilt kanseri görülme oranında önemli bir artış gözlemlenmektedir. Cilt kanserinin zamanında ve doğru bir şekilde teşhis edilmesi, tedavi başarı oranlarını artırmakta ve aynı zamanda hastaların yaşam kalitesinin iyileşmesine büyük katkı sağlamaktadır. Geleneksel cilt kanseri tanı yöntemleri genellikle görsel değerlendirmelere dayanmakta ve öznel bir yaklaşım içermektedir. Bununla birlikte, derin öğrenme algoritmaları, cilt kanseri teşhislerinin doğruluğunu ve verimliliğini artırmak için etkili çözümler sunmaktadır. Bu çalışmada, EfficientNet, VGG, Inception, DenseNet ve DarkNet gibi gelişmiş Evrişimsel Sinir Ağı (CNN) modellerinin cilt kanseri sınıflandırmasındaki performansları incelenmiştir. Toplamda yirmi bir CNN modeli, ISIC 2017 veri seti üzerinde, veri artırma ve transfer öğrenme teknikleri kullanılarak eğitilmiş ve detaylı bir şekilde değerlendirilmiştir. Deneysel sonuçlar, EfficientNet-b0 modelinin %84.00 doğruluk, %83.63 kesinlik, %74.96 duyarlılık ve %78.59 F1-skoru ile en yüksek performansı sergilediğini göstermiştir. Bu kapsamlı analiz, CNN tabanlı modellerin cilt kanseri teşhisindeki etkinliğini göstermekte ve gelecekteki araştırmalar için bu algoritmaların potansiyelini ortaya koymaktadır.

Keywords

Cilt kanseri sınıflandırması, Evrişimsel sinir ağları, Melanom

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