Deep Learning Approach to Improve Skin Lesion Classification for Early Skin Cancer Detection Erken Cilt Kanseri Tespiti icin Cilt Lezyonu Siniflandirmasini Iyilestirmek Icin Derin Ogrenme Yaklasimi

Nairi C., BİLGİN G.

33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025, İstanbul, Turkey, 25 - 28 June 2025, (Full Text) identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/siu66497.2025.11111771
  • City: İstanbul
  • Country: Turkey
  • Keywords: Deep learning, ensemble learning, medical image analysis, skin lesion classification, transfer learning
  • Yıldız Technical University Affiliated: Yes

Abstract

Skin cancer is a common and deadly disease, highlighting the need for early detection. This study evaluates five CNN architectures (DenseNet201, EfficientNetB0, XceptionNet, ResNet50, InceptionV3) and several Vision Transformer (ViT) models (ViT, Swin Transformer V2, DINOv2, PVT, ViT Hybrid) using the HAM10000 dataset. A 5-fold cross-validation assesses performance, and two Weighted Voting ensemble methods - one for CNNs and one for ViTs - are applied to enhance accuracy. Results are compared with transfer learning on ResNet50 and EfficientNetB0, showing that ensemble methods improve classification performance for early skin cancer detection.