# 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
#### 2.2.2 Feature Extraction and Multiple Model-Based Architectures  
Advancements in radiographic imaging (mammograms, CT scans, MRI) have made it possible to diagnose breast cancer at an early stage, but analysis by experienced radiologists and pathologists can be costly and error-prone [20]. Computer vision and ML, particularly DL, show potential in detecting and classifying BC using these images, as they can efficiently handle large amounts of data and automatically extract features.  
In order to achieve a robust model for accurately detecting BC, [PERSON] et al. [21] used a well-established dataset of multi-instance mammography images to train multiple models (AlexNet, VGG-16, VGG-19, ResNet-18, ResNet-34, ResNet-50, ResNet-101), ResNet-18 being the one with the best results. It is accompanied by some custom layers for feature extraction and a feature fusion module. A three-layer fully connected network was used as the classifier, achieving an accuracy of 85.48% and an AUC of 0.884. The study showed the effectiveness of using multi-instance mammography datasets for BC detection, but additional images could improve the results.  
An ensemble model for BC diagnosis based on mammography images was described by [PERSON] et al. [22] as they used a CNN and a feature extraction technique. The preprocessing part encompassed the median filter for noise removal as well as image enhancement for improving the contrast. The ensemble of a nine-layer CNN and the feature extraction method (identifying the texture features and diminishing their dimensions based on uniform manifold approximation and projection--UMAP technique) had good performances: an accuracy of 96.00% for the Mammographic Image Analysis Society (MIAS) repository and 97.90% accuracy for the DDSM dataset.  
A simplified CNN architecture for feature learning and fine-tuning was proposed by [PERSON] et al. [23] to classify mammography images into normal or malignant. The DDSM repository was used as it contains 2,260 images which were fed into the proposed CNN architecture that consists of 18 layers. Considering its simple CNN architecture, the model achieved good performance with an accuracy of 92.84%.  
Another evaluation of DL-based AI techniques for detecting BC on digital mammogram images was performed by [PERSON] et al. [24], testing the effect of different data-processing strategies and backbone DL models. The best result achieved an AUC of 0.8979 and an accuracy of 0.8178 by using certain DL models, combining global and local features, and leveraging background cropping, text removal, contrast adjustment, and more training data.  
[PERSON] et al. [25] proposed a CNN hybrid system for breast ultrasonography image classification. The system was based on the Alexnet, MobilenetV2, and Resnet50 models, which were used for feature extraction. Further, the features were concatenated, and the mRMR (Minimum Redundancy Maximum Relevance) algorithm was applied for feature selection. Finally, the classification step was performed through SVM and KNN classifiers.  
DL-based techniques have shown promising results in many medical image analysis applications. Feature extraction techniques can help in identifying the most relevant features that have the greatest impact on the output, thereby improving the overall performance of the model and its generalization by reducing overfitting and increasing the model's ability to generalize to new and unseen data.  
Training and testing multiple model-based architectures can help improve the performance, generalization, and adaptability of models by identifying the proper model that best understands the data and is the most robust to changes in the data distribution. However, it is also important to consider the potential disadvantages and trade-offs involved in their use, such as loss of information or bias in feature selection; they are also time-consuming and at risk of overfitting.