## 2 Literature Review
In this architectural model, the F1 score was 0.8238, accuracy was 0.8787, precision was 0.8381, and recall was 0.8762. Similarly, the MSE and MSA values in this architecture were measured as 0.1195 and 0.2497, respectively [16]. [PERSON] et al. presented a new deep feature extraction model for detecting breast cancer subtypes based on a TL model using the BreakHis dataset. As a result of using cubic support vector machines (SVM) as classifiers in the final stage of their proposed DRNet model, this model gave average accuracy rates of 98.61%, 98.04%, 97.68%, and 97.71% at 40\(\times\), 100\(\times\), 200\(\times\), and 400\(\times\) zoom rates, respectively [17]. [PERSON] and [PERSON] proposed the Inception-ResNet-V2 model in which Inception and ResNet models are used together to detect breast cancer tumors using DL methods. In this study, they aimed to facilitate classification by determining the location and regional density of the cancerous region by masking and filling the mammography images in the dataset during data pre-processing. They then performed classification in the Inception-ResNet-V2 model. Because of this classification, they obtained a 96.21% accuracy rate, 97.48% recall value, 98.18% precision value, and 97.83% F1 score [18]. [PERSON] and [PERSON] used breast cancer histopathology images with 40\(\times\), 100\(\times\), 200\(\times\), and 400\(\times\) zoom ratios to classify breast cancer in their study. They proposed deep learning models supported by pretrained networks. Unlike previous studies in the literature, these studies compared pre-trained models among themselves by making additions such as data duplication and data reduction. The ResNet50, Xception, InceptionV3, and DenseNet201 models were recommended for this study. The Xception model, which uses a 200\(\times\) zoom ratio, was among the models that produced the best results. The proposed model achieved a success rate of 98.01% [19]. In another study, [PERSON] and [PERSON] classified breast cancer images using a dataset known as BreastHis. They improved a new hybrid model called the VIHist network using the VGG16 and InceptionV3 models from the pretrained CNN models. In this hybrid model, they achieved an accuracy of 99.03% by performing classification without manually extracting any features [20]. [PERSON] et al. conducted a study to classify breast cancer as benign or malignant. Two models were developed with the aim of making the classification independent of the image zoom ratio. While the first model predicted only malignant tissue, the second model simultaneously predicted both malignant tissue and image magnification. These models achieved 83.25% and 80.10% accuracy rates, respectively [21]. [PERSON] et al. proposed a scheme to classify breast histopathology images using two architectures, IDCDNet and IDCNet, which are similar to the VGGNet architecture. As a result of this study, they stated that the IDCNet architecture demonstrated the best performance with an accuracy rate of 87.13% [22]. [PERSON] classified breast cancer as IDC (invasive ductal carcinoma) and non-IDC using a dataset comprising breast histopathological images. In this study, the dataset was normalized to include the same number of IDC and non-IDC images to eliminate the imbalance in the dataset. In this study, CNN architecture was trained for 100 epochs, and it reached maximum performance with an accuracy of 87.47% after the 88 th epoch. When running it for 100 epochs to validate the CNN architecture he created on the test dataset, he observed that the model gave the highest accuracy rate (89.56% accuracy) after the 94 th epoch [23]. In the automatic detection of breast cancer by [PERSON] et al., a dataset containing the whole breast images of 162 female patients was used. In this study, researchers compared the feature extraction approach in deep learning, specifically by performing manual feature extraction with a CNN. In this approach, where manual feature extraction was used, the model was evaluated using the F1 score and balanced accuracy ratio.