# Classification of Breast Cancer in Mammograms with Deep Learning Adding a Fifth Class
## 2 Related Work  
Research in the early detection of breast cancer by imaging is a large area of study in which there is extensive research on the subject. Using ML techniques to classify masses and calcifications, [PERSON] et al. implemented a semi-automatic segmentation method in a training group of 1000 images with 677 benign and 323 malignant lesions. Testing various neural networks models and a DL model, they got 85.8%, 84.3%, 74.0%, and 89.7% accuracy for the SVM, KNN, LDA, and the DL model, respectively. Although, there is research made on histopathological images using the Breast Cancer Wisconsin database. It contains 569 records of 32 characteristics of benign and malignant tumors. [PERSON] and [PERSON] [28] researched five features using principal component analysis (PCA) and random forest to classify the lesions. They conclude that the best feature selection methods are the recursive selection with cross-validation that achieves 97.34% accuracy and the recursive selection of features with 95.8% accuracy. Another investigation [29], used 75 lumbar images to develop a classifier method of the image quality perceived by radiologists and then obtain characteristics of the images to feed a classification algorithm and validate with another 20 images remaining from their data set. In their experiment, to classify the quality of the images, three neuroradiologists established the criteria for evaluating the image to classify it as good, poor, fair, reasonable, and excellent. Using a parallel system, they extract the characteristics and classify with machine learning techniques such as LDA, quadratic discriminant analysis (QDA), SVM, logistic regression, and MLP, obtaining accuracies between 70% and 77% with a recall of 82%, and an area under the curve (AUC) 77% at best. The work of [PERSON] et al., 2021 [30] proposes a method of classifying multiple classes of lung diseases with CNN ImageNet, applying fine-tuning with their initial weights, and feeding the network with images in *.tif format. The datasets used were the National Institutes of Health (NIH) of EE, divided into Normal, Pneumonia, and Pneumothorax, and the Cheonan Soonchunhyang University Hospital dataset that includes Tuberculosis. To improve performance, they used the Center Crop technique in pre-processing while maintaining the image's aspect ratio in the training procedure; they also updated the training weights for classes with a more significant number of images and performed data augmentation. As a result, they obtained a performance of 85.32%. The predictions of four classes measured with data from the Soonchunhyang University Hospital in Cheonan had a mean precision of 96.1%, a mean sensitivity of 92.2%, a mean specificity of 97.4%. In the work of [PERSON] et al., 2021 [31], they improved mammography using the medium, Gaussian, and bilateral filters to apply the Contrast Limited Adaptive Histogram Equalization method (CLAHE) later and the Otsu method to cut out the breast area. Regarding training, they used 7259 mammography images from MIAS, INbreast, and one private dataset, of which they divide 87.4% for training (6346) and the rest (913) for tests. They performed data augmentation in seven different ways to train several fine-tuned CNNs in parallel and classify masses and microcalcifications in mammograms. Through transfer learning, they changed the CNN final layers, keeping the initial weights intact. The CNNs they used were VGGNet, MobileNet, GoogLeNet, ResNet, DenseNet and proposed a deep hybrid network ConvNet + SVM. After training and testing with the hybrid network, they obtained an accuracy of 97.8%, AUC 91.4%, and F1-score of 97.06%. In [32], they compared 14 different neural networks applied on diverse databases to identify which method can perform better classification of malignant breast cells, concluding that the CNN offers slightly higher precision than the multi-layer perceptron neural network (MLP). [PERSON] et al.