# Applying Deep Learning Methods for Mammography Analysis and Breast Cancer Detection
## 2 Review of Medical Imaging Techniques for BC Detection
### Deep Learning Methods for BC Detection and Classification
#### 2.2.1 Transfer Learning and Data Augmentation  
[PERSON] et al. [9] proposed a tumor detection model using transfer learning and data augmentation to prevent overfitting. They used three pre-trained CNN architectures: AlexNet, VGG16, and VGG19 on a dataset of 4000 pre-processed images from two databases (Digital Database for Screening Mammography--DDSM and Chinese Mammography Database--CMD). A data augmentation approach was applied to improve model quality. AlexNet and VGG16 achieved great performances: between 99.5% and 100% accuracy, respectively, with trainable layers. However, using the models without training the specified layers led to poor performances and insufficient results. An end-to-end training technique with a DL-based algorithm to classify local image patches was proposed in [10]. They trained a patch classifier using DDSM, a fully annotated dataset with regions of interest (ROI), and then transferred the learning to the INbreast dataset [11]. VGG-16 and ResNet-50 were used as classifier models in two steps: patch classifier training followed by whole-image classifier training. Transfer learning on the INbreast test set resulted in the best performance, with an area under the receiver operating characteristics curve (AUC) of 0.95. A framework for BC image segmentation and classification to assist radiologists in early detection and improve efficiency is proposed in [12]. This utilizes multiple DL models including InceptionV3, DenseNet121, ResNet50, VGG16, and MobileNetV2, to classify mammographic images as benign or malignant. Additionally, a modified U-Net model is used for breast area segmentation in Cranio Caudal (CC) and Mediolateral Oblique (MLO) views. Transfer learning and data augmentation are used to overcome the challenge of the lack of tagged data. The challenges of training DL models for medical imaging due to the limited size of freely accessible biomedical image datasets and privacy/legal issues are highlighted in [13]. Overfitting is a common problem due to the lack of generalized output. Data augmentation is used to increase the training set size using various transformations. The manuscript surveys different data augmentation techniques for mammogram images to provide insights into basic and DL-based augmentation techniques. Another proposed approach for BC detection and classification comprises five stages including contrast enhancement, TTCNN architecture, transfer learning, feature fusion, and feature selection [14]. The method achieved high accuracies of 99.08%, 96.82%, and 96.57% on DDSM, INbreast, and MIAS datasets, respectively, but performance may vary on different images due to factors such as background noise or overfitting. In addition to the previous related studies, BreastNet18 is a method proposed in [15] and it is based on a fine-tuned VGG16 with customized hyper-parameters and layer structures on a previously augmented CBIS-DDSM dataset [16]. An ablation study was also performed on the results, testing different batch sizes, flattening layers, loss functions or optimizers, followed by the most representative parameters. Transfer learning was also applied in [17]. The conducted study proposes a DL method using CNN to classify mammography regions, achieving an accuracy of 97.8%. Data cleaning, preprocessing, and augmentation improved the accuracy of mass recognition. Two experiments were conducted to validate the consistency of the model, using five different deep convolutional neural networks and a support vector machine algorithm. A standard system that describes the mammogram output is represented by the breast imaging reporting and data system (BI-RADS). A DNN-based model that accurately classifies mammograms into eight BI-RADS categories (0, 1, 2, 3, 4A, 4B, 4C, 5) is presented by [PERSON] et al. [18]. The model was trained using block-based images and achieved an overall accuracy of 94.22%, an average sensitivity of 95.31%, an average specificity of 99.15%, and an AUC of 0.9723.