## 2 Literature Review
In a study by [PERSON] et al., they proposed a hybrid system based on feature fusion, BoW (Bag of Words), and Deep Neural Networks (DNN) methods for the binary classification of breast cancer using breast histopathology images. The proposed hybrid method achieved 94.5% and 80.8% accuracy on the training and test datasets, respectively [10]. [PERSON] et al. developed a 2D CNN model with three optimized hidden layers for breast cancer classification. This model achieved 95% accuracy, and a 0.94 F1 score in binary classification between benign and malign cancerous images [11]. [PERSON] proposed a new Fully Convolutional Network (FCN) model for breast cancer classification using a public dataset containing breast ultrasound images. [PERSON], who also used the U-Net model in his study, compared the performances of these two models. He obtained average results as a result of five experiments and obtained a 0.772 mean IoU, 0.716 precision score, 0.777 recall score, and 0.745 F1 score for the FCN model. In the U-Net model, a 0.764 mean IoU, 0.676 precision score, 0.804 recall score, and 0.730 F1 Score were obtained [12]. [PERSON] et al. used two different datasets containing ultrasound images for breast cancer classification. One of these datasets has two classes, and the other dataset has three classes. Using the data augmentation method on these datasets, the authors examined the performance increase in ResNet18 and VGG16 models. The dataset with two classes gave an accuracy rate of 84.5% in the ResNet18 model, and after applying the data augmentation method, the model yielded an accuracy of 88.2%. When the same process was applied to the dataset with the three classes, it gave an 82% accuracy rate and 82.5% accuracy by applying the data augmentation method. In the VGG16 model, the dataset with two classes obtained an accuracy rate of 94.33% before the data augmentation method was applied and an accuracy rate of 97.8% after the data augmentation method was applied. The dataset with the three classes obtained an accuracy rate of 86.5% without the data augmentation and 90% after the data augmentation method [13]. [PERSON] et al. conducted a study to reduce false-positive and false-negative rates in breast cancer diagnosis and proposed a DL-based model. Their proposed model provides high accuracy, efficiency, and consistency by using multiple image mammograms. The proposed model, which includes unsupervised feature extraction with a variational autoencoder (VAE) and classification steps with a CNN, demonstrated high performance on both local and public datasets. The proposed model was able to reduce the false positive call rate from 6.13% to 2.61% and achieved an AUC of 0.98 on the INbreast dataset [14]. In this study, [PERSON] et al. optimized the hyperparameters of the pre-trained VGG16 model using the particle swarm optimization (PSO) algorithm to classify breast cancer. After this optimization process, the features of the images in the dataset were extracted using the PSO-optimized VGG16 model. They then trained these features using fully connected layers and a LR classifier to perform breast cancer classification. Their hybrid model yielded 95.7%, 94.1%, 96.9%, and 91.2% accuracy on the BreakHis dataset at 40\(\times\), 100\(\times\), 200\(\times\), and 400\(\times\) magnifications, respectively [15]. [PERSON] and [PERSON] employed a CNN architecture with three convolutional layers, three ReLu layers, three pooling layers, and one fully connected layer for breast pathology images classification. They tried various filter sizes and filter numbers on this CNN architecture. In addition, to measure the success of the architecture, the F1 score, accuracy, precision, and recall values were expected to approach the value of 1, and the mean squared error (MSE) and mean square approximation (MSA) values were expected to approach the value of 0. The architectural model with the highest success has a 3 \(\times\) 3 filter size and 32 filters.