# Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review
## 2 Materials and Methods
### Study Design
#### 2.2.3 Preprocessing  
It is known that the database characteristics can significantly affect the performance of a CAD scheme, or even a particular processing technique. Furthermore, it can develop a scheme that yields erroneous or confusing results [104] since radiological images contain noise, artefacts, and other factors that can affect medical and computer interpretations. Thus, the first step in preprocessing is to improve the image quality, contrast, removal noise, and pectoral muscle [105].  
Image Enhancement  
The main purpose of image preprocessing is to enhance the image and suppress noise while preserving important diagnostic features [106, 107]. Preprocessing for breast cancer diagnosis also consists of delineation of the tumors from the background, breast border extraction, and pectoral muscle removal. The pectoral muscle segmentation is a challenge in mammogram image analysis because the density and texture information is similar to that of the breast tissues. Furthermore, it depends on the standard view used during mammography. Generally, mediolateral oblique (MLO) and craniocaudal (CC) views are used [78].  
As noted, DM includes many sources of noise, which are classified as a high-intensity, low-intensity, or tape artefacts. The principal noise models observed in mammography are salt and pepper, Gaussian, speckle, and Poisson noise.  
In the same way, US images suffer from noise, such as intensity inhomogeneity, a low signal-to-noise ratio, high speckle noise [108, 109], blurry boundaries, shadow, attenuation, speckle interference, and low contrast. Speckle noise reduction techniques are categorized in filtering, wavelet, and compound methods [12].  
Thus, many traditional filters can be applied for noise removal, including a wavelet transform, median filter, mean filter, adaptive median filter, Gaussian filter, and adaptive Wiener filter [3, 110, 111, 112, 113]. Furthermore, different traditional methods, such as histogram equalization (HE) [114, 115],  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Models** & **Description** & **Application** \\ \hline \multirow{8}{*}{[86]} & & Feature selection methods: wrapper and filter (chi-square [90]). The most well-known feature extraction techniques are PCA, LDA, GLCM, gain ratio, recursive feature [91], RF, WPT [92, 93], Fourier power spectrum [94], Gaussian [95] and DBT [29]. PCA feature extraction techniques are better at reducing the high-dimensional correlated features into low dimensional features [87]. & Feature Selection and extraction \\ \hline \multirow{8}{*}{[86]} & & SVM is useful in DM classification because these are highly overlapping and nonlinear in their feature space. It minimizes the generalization error during the process of testing data and is much more accurate and computational efficient because of the reduced number of parameters. ANN: Backpropagation, SOM, and hierarchical ANN. The performance of back-propagation is better than that of linear classifiers. & Classification \\  & & & However, the training process is stochastic and unrepeatable, even with the same data and initial conditions. Prone to overfitting due to the complexity of the model structure. Finally, advantages and disadvantages from other classifiers have been previously discussed in several studies: KNN [100], BDT [101], simple logistic classifier [102], and DBN [103] \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._adaptive histogram equalization (AHE) [116], and contrast-limited adaptive histogram equalization (CLAHE) [117], can be used to enhance the image.  
On the other hand, deep CNNs [118] are gaining attention for improving super-resolution [119] images (SR) based on a CNN, namely, (i) multi-image super-resolution and (ii) single-image super-resolution [120, 121]. Among the most used algorithms for generating high-resolution (HR) imaging [122, 123] are nearest-neighbor interpolation [124], bilinear interpolation [125], and bicubic interpolation [126].