## 2 Literature Review
They observed that these two measurement values yielded rates of 71.80% and 84.23%, respectively [24]. [PERSON] and [PERSON] used an automatic machine learning (AutoML) method to classify breast cancer histopathological images as positive or negative for IDCs. In this study, they increased the dataset to obtain a total of 397,524 images. While 363,396 of these images were used for the AutoML model, they used 34,128 for the holdout method. They reserved the images for the AutoML allocating 80% for training, 10% testing, and 10% validation. In this experimental study, they obtained an average accuracy of 91.6%. They obtained a balanced accuracy rate of 84.6% in the data used for the holdout method [25]. [PERSON] and [PERSON] presented a new approach using a dataset comprising breast histopathological images in their IDC breast cancer diagnosis study. Their proposed approach is the deep residual neural network (DRNN). Within the scope of the study, 3000 images in the dataset were selected as IDC positive and 4500 as IDC negative. In the preprocessing step of the dataset, they created a subset and chose different color spaces and color channels from the CIELAB (Commission Internationale de l'Eclairage LAB) color space. They obtained an image matrix with seven channels by combining the four different color channels they selected with the original red-green-blue (RGB) color space to use in model training. They used the Gaussian blur method to remove possible noise from the images. In the last stage, to improve noise, the authors made the dataset suitable for model training using the contrast-limited adaptive histogram equation and contrast-limited adaptive histogram equalization (CLAHE) method. They used 6000 of 7500 images in the dataset, which were suitable for model training and testing. They used 600 of the 6000 images reserved for model training to determine model accuracy. As a result of the study, their proposed DRNN method obtained an accuracy rate of 99.29% [26]. [PERSON] et al. used CNN on the classification of IDC breast cancer. They performed classifications on the VGG16, ResNet18, DenseNet, and CancerNet models that were pretrained on the dataset. They compared the classification results of the CNN model they proposed. The proposed CNN model provided better results than the previously trained models with an accuracy of 92% [27]. [PERSON] et al. classified breast cancer using a CNN on a dataset containing histopathological images of breasts. In this study, the model was tested on 1728 images. As a result of this study, the CNN model obtained an accuracy of 87.64% [28]. [PERSON] et al. used transfer learning models to classify breast cancer. The AlexNet, VGG19, InceptionV3, Xception, and GoogLeNet models were considered. A total of 27,800 images with and without IDCs were randomly selected from 277,524 images in the dataset and used for training and testing the models. As a result of this study, the AlexNet, VGG19, InceptionV3, Xception, and GoogLeNet models obtained 96.74%, 94.83%, 92.48%, 90.72%, and 97.80%, respectively [29]. [PERSON] et al. used deep learning models to compare two models in their breast cancer classification study. The deep learning models used in this study were the DenseNet-201 and Xception models. They trained, tested, and validated the models using 31,827 images selected from 277,254 images in the dataset. As a result of this study, the DenseNet-201 and Xception models yielded 96.74% and 96.69% accuracy rates, respectively [30]. [PERSON] et al. classified breast cancer using machine learning and deep learning using a dataset containing breast histopathology images. Unlike other studies, they used two new datasets created by a random selection of 277,524 images to compare the classifications. They referred to an unbalanced class distribution in the first dataset and used a dataset consisting of 65,279 non-IDCs and 24,721 IDC-containing images. They used LeNet, AlexNet, VGG19, VGG16, ResNet50, SVM, and Twin SVM models in their classification task on the unbalanced dataset.