Akademik Veri Yönetim Sistemi
2 nd International Conference on Engineering, Natural and Social Sciences, Konya, Türkiye, 4 - 06 Nisan 2023, cilt.1, sa.1, ss.658-666
Skin cancer is one of the most common types of cancer in the world and an important public
health problem. Rapid diagnosis and correct treatment are important in the treatment of skin cancer.
However, the in-depth imaging and analysis required to diagnose skin cancer is a time-consuming process
that requires expertise. Unfortunately, the lack of labeled data in the classification and the limited size of
training datasets affect the performance of deep learning architectures and the correct diagnosis of the
disease. In this study, cancer lesion classification was performed by using deep convolution networks with
a GAN (Generative Adversarial Network) based data augmentation method for skin lesion classification.
This model synthesizes new skin lesion images by analyzing skin lesion images in training data. The images
synthesized by GAN are processed by the classification model in the same way as the real images and added
to the training set for better classification performance. In all classification tasks, 3 different convolutional
neural network architectures were used for classification and feature extraction. The classification accuracy
of the architectures consisting of images obtained with the ISIC (International Skin Imaging Cooperation)
Archive dataset is 86.00% for CNN, 90.81% for MobileNet, and 81.66% for ResNet-18, respectively. The
performance of the models in the hybrid dataset consisting of the combination of synthetic images and the
original primary dataset increased to 86.34% for CNN, 91.67% for MobileNet and 91.78% for ResNet-18.
This study shows that GAN-based data enhancement models in skin lesion classification also make an
important contribution to medical image analysis.