Görüntü artırma tekniklerinin cilt kanseri türleri üzerinde evrişimsel sinir ağları ile sınıflandırma başarılarının karşılaştırılması

Year 2023, Volume: 12 Issue: 4, 1141 - 1156, 15.10.2023

Ömer Özcan , Muhammed Karaaltun

https://doi.org/10.28948/ngumuh.1270466

Abstract

Derin öğrenme yaklaşımlarından evrişimsel sinir ağları algoritması ile görüntü veri setleri üzerinde sınıflandırma çalışmaları yaygın olarak tıp ve tarım gibi birçok alanda başarılı bir şekilde yapılmaktadır. Ancak, görüntü veri setleri içerisinde bulunan sınıfların örnek sayıları dengesiz olduğu durumlarda bu algoritmanın sınıflandırma başarımı olumsuz yönde etkilenmektedir. Genelde çoğunluk sınıfının aksine azınlık sınıfı(ları) evrişimsel sinir ağları algoritması tarafından iyi bir şekilde öğrenilmemektedir. Bunun gibi durumlarda aşırı örnekleme yöntemlerine başvurmak başarılı sonuçlar alınmasını sağlamaktadır. Aşırı örnekleme yöntemleri ile azınlık sınıfı(ları) örneklerinin sayısı artırılarak çoğunluk sınıfının örnek sayısına yakın ya da eşit olmaktadır. Bu çalışmada literatürde sıkça kullanılan; yer değiştirme, döndürme, rastgele silme, gürültü ekleme, resimlerin karıştırılması, çekirdek filtreleri, çekişmeli üretici ağlar, çevirme, özellik uzayı dönüşümü, kırpma ve renk uzayı dönüşümü aşırı örnekleme yöntemleri Ham10000 veri seti üzerinde uygulanmıştır. Uygulama sonucunda elde edilen sonuçlara göre sınıflandırma başarısı açısından aşırı örnekleme yöntemleri karşılaştırılmıştır. Üç farklı evrişimsel sinir ağları modellerinden; ResNet50, DenseNet201, MobileNet ile elde edilen sınıflandırma sonuçlarına göre doğruluk açısından ResNet50 modelinde gürültü ekleme yöntemi 0.967, DenseNet201 modelinde renk uzayı dönüşümü yöntemi 0.965 ve MobileNet modelinde ise Resimlerin karıştırılması yöntemi 0.974 sınıflandırma başarısı değeri ile diğer aşırı örnekleme yöntemlerinden daha iyi bir sonuç elde etmiştir.

Keywords

Aşırı örnekleme, derin öğrenme, görüntü, yetersiz örnekleme, evrişimsel sinir ağı.

Comparison of the classification perfomance of image augmentation techniques with convolutional neural networks on skin cancer types

Abstract

Classification studies on image data sets with convolutional neural networks algorithm, which is one of the deep learning approaches, are widely performed successfully in many fields such as medicine and agriculture. However, in cases where the sample numbers of the classes in the image datasets are imbalanced, the classification performance of this algorithm is negatively affected. In general, unlike the majority class, the minority class(es) are not well learned by the convolutional neural network algorithm. In such cases, applying oversampling methods provides successful results. With oversampling methods, the number of minority class(s) samples is increased, making it close to or equal to the sample number of the majority class. In this study, translation, rotation, random erasing, noise injection, mixing of images, kernel filters, generative adversarial networks, flipping, feature space transformation, cropping and color space transformation oversampling methods frequently used in the literature were applied on the Ham10000 dataset. According to the results obtained as a result of the study, oversampling methods have been compared in terms of classification performance. From three different convolutional neural network models, according to the classification results obtained with ResNet50, DenseNet201, MobileNet, in terms of accuracy, the noise injection method in the ResNet50 model was 0.967, the color space transformation method in the DenseNet201 model was 0.965, and in the MobileNet model, the mixing of images method had a classification performance value of 0.974 which was better than the other oversampling methods.

Keywords

Convolutional neural network, Deep learning, Image, Oversampling, Undersampling

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