Derin Öğrenme ile Artırılmış Görüntü Seti üzerinden Cilt Kanseri Tespiti

Abstract

Bu çalışmada, son yıllarda görüntü sınıflandırmada artan oranda ilgi gören derin öğrenme ve görüntü işleme yöntemleri kullanılarak kötü huylu (malignant) cilt lezyonlarının erken teşhisini kolaylaştırıcı yapay zekâ tabanlı sınıflandırma deneyleri gerçekleştirilmiştir. Melanom, en kötü huylu ve az görülen bir kanser türü olduğundan dolayı derin öğrenme mimarisini eğitmek için yeterli sayıda eğitim ve test görüntüsü bulmak zordur. Bu nedenle artırılmış veri seti oluşturulmuş ve 6 farklı derin öğrenme mimarisi ile eğitim yapılmıştır. Kötü huylu ve iyi huylu cilt lezyonlarını sınıflandırmak için popüler olan AlexNet, DenseNet-121, ResNet-18, ResNet-34, SqueezeNet ve VGGNet-16 mimarileri kullanılmıştır. Deneyler HAM10000 veri seti üzerinde artırma yapılarak gerçekleştirilmiştir. Deneyler sonucunda en başarılı sonuçları veren Resnet-34 mimarisi ile ortalama %87,5 doğruluk oranı, %94 AUC skoru, %84,5 F-skoru, %87,6 kesinlik değeri elde edilmiştir. Diğer derin öğrenme mimarilerinden elde edilen sonuçlar ve karşılaştırmalı analizler de çalışmada ayrıca sunulmuştur.

Keywords

• Derin öğrenme
• Evrişimsel sinir ağları
• Transfer öğrenme
• Cilt kanseri
• Sınıflandırma

Skin Cancer Detection via Augmented Image Set with Deep Learning

Abstract

In this study, artificial intelligence-based classification experiments were carried out to facilitate the early diagnosis of malignant skin lesions by using deep learning and image processing methods, which have received increasing attention in image classification in recent years. Because melanoma is the most malignant and rarest type of cancer, it is difficult to find enough training and test images to train the deep learning architecture. For this reason, augmented data set was created and training was conducted with 6 different deep learning architectures. Popular architectures AlexNet, DenseNet-121, ResNet-18, ResNet-34, SqueezeNet and VGGNet-16 were used to classify malignant and benign skin lesions. The experiments were carried out on the HAM10000 data set by augmented. As a result of the experiments, with the Resnet-34 architecture, which gave the most successful results, an average of 87.5% accuracy, 94% AUC score, 84.5% F-score, and 87.6% precision were obtained. The results and comparative analyzes obtained from other deep learning architectures are also presented in the study.

Keywords

• Deep learning
• Convolutional neural network
• Transfer learning
• Skin cancer
• Classification

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