Özet:

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.

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