Automatic Classification of Melanoma Skin Cancer Images with Vision Transform Model and Transfer Learning

Abstract

Melanoma is one of the most aggressive and lethal forms of skin cancer. Therefore, early diagnosis and correct diagnosis are very important for the health of the patient. Diagnostic procedures require human expertise, increasing the possibility of error. With developing technology, advances in deep learning models have become hope for the automatic detection of Melanoma skin cancer with computer systems. The Vision Transformer (ViT) model was developed by Google and has achieved very successful results in the field of classification. In this study, the transfer learning method was applied with the ViT model using the melanoma skin cancer dataset taken from the Kaggle library and the performance of the model was evaluated. Before starting training, pre-processing was applied to the data set. The dataset consists of 9600 training and 1000 test images. Training and experimental testing of the model was carried out with Python language on the Colab platform. As a result of the experimental studies conducted on the test data set, it was seen that the model reached an accuracy rate of 93.5% and was competitive with existing models

Keywords

• Melanoma
• Deep learning
• Vision Transformer
• Classification

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