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. Cancer diagnosis is made by field experts and this increases the possibility of error. Today, with the developing deep learning technology, it has been seen that automatic detection of Melanoma skin cancer can be performed with high accuracy by computer systems. One of the latest technologies developed in the field of deep learning is the Vision Transformer (ViT) model. This model was produced by Google and has achieved very successful results in the field of classification. This study aims to detect melanoma skin cancer with high accuracy using the ViT model. In the study, the melanoma skin cancer dataset consisting of 9600 training and 1000 test images in the Kaggle library was used. In order to use the data set more effectively, some pre-processing methods were first applied. Model performance was evaluated using the transfer learning approach together with the ViT model on this data set. Training and experimental testing of the model was carried out with Python language on the Colab platform. As a result of the experimental studies carried out on the test data set, it was seen that the model reached 93.5% accuracy rate. This rate is competitive and promising when compared to existing models in the literature.