## 2 Literature Review
They stated that the accuracy rates of these models on the unbalanced dataset were 73%, 79%, 81%, 85%, 88%, 86%, and 73%, respectively. In the second dataset, they preferred a balanced class distribution and therefore used 5547 images, which they randomly selected from the original dataset containing 2788 and 2759 IDCs from two separate classes, respectively. They used LeNet, AlexNet, VGG19, VGG16, ResNet50, SVM, and Twin SVM models, which they previously used on an unbalanced dataset, in their study on a dataset with balanced class distribution. They stated that the accuracy rates of these models on the balanced dataset were 65%, 72%, 73%, 74%, 78%, 76%, and 62%, respectively [31]. The conclusions of this study are as follows: classification using an unbalanced dataset gave better results than classification using a balanced dataset, and the ResNet model gave the best results for both classification methods. [PERSON] et al. conducted a breast cancer classification study using a CNN and a dataset of histopathological images. Unlike other studies in the literature, the authors resized the images to be 48 \(\times\) 48 instead of using their original size. With the data preprocessing step, the images were made suitable for training in the CNN model using the color constancy technique on the images and the histogram equalization method. The model they obtained using CNN with color constancy gave an AUC of 0.935. The CNN model they obtained using the histogram equalization method gave an AUC of 0.876 [32]. [PERSON] et al. proposed a CNN model for the early detection of breast cancer in a study they conducted using a dataset of breast histopathology images. In the dataset preprocessing step, the images were converted to grayscale and any noise that may exist was removed. As a final step, they performed classification using the CNN model by extracting various features, such as energy, entropy, randomness, correlation, and homogeneity, from the images. The CNN model suggested by the authors gave an accuracy rate of 90% [33]. [PERSON] and [PERSON] used the ResNet model, which is a deep learning method, in their study to classify breast cancer. By selecting random images representing different numbers from the dataset, they divided the dataset into subsets and designated these subsets as training and test data at different rates. For this purpose, they first created a dataset containing 157,572 images, 141,814 of which were IDC-positive images and 15,758 of which were IDC-negative images, and separated the dataset into 90% training and 10% test data. Classification studies were conducted using this model together with the ResNet50 and ResNet34 models. These models obtained 91% and 90% accuracy on this dataset, respectively. They divided the dataset, which consisted of 118,180 IDC-positive images and the remaining 39,392 IDC-negative images, into 75% training and 25% test data. In their classification study, the ResNet50 and ResNet34 classification models obtained 88% and 86% accuracy, respectively. Finally, in their study, they divided a dataset containing 78,786 IDC positive and 78,786 IDC negative images (placing 157,275 images into 50% training and 50% test datasets) and then used them to train the ResNet50 and ResNet34 models. Because of this classification, both models obtained an accuracy rate of 79% [34]. In his study on the diagnosis of breast cancer, [PERSON] included a dataset containing histopathological breast images. In this study, he classified the IDC breast cancer dataset using the MobileNetV2 and EfficientNet models. This resulted in test accuracy rates of 92.35% and 91.02% for the classification models, respectively [35]. In response to findings from recent literature, many studies have investigated the classification of breast cancer using various breast image datasets. There are main features of this study that distinguish it from other studies in the literature. First, this study analyses the effect of datasets of different sizes on the performance of deep learning models.