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23 November 2020

Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review

,
and
1
Departamento de Química y Ciencias Exactas, Universidad Técnica Particular de Loja, San Cayetano Alto s/n CP1101608, Loja, Ecuador
2
Instituto de Instrumentacion para la Imagen Molecular I3M, Universitat Politécnica de Valencia, E-46022 Valencia, Spain
3
Theoretical and Experimental Epistemology Lab, School of Optometry and Vision Science, University of Waterloo, Waterloo, ON N2L3G1, Canada
4
Department of Systems Design Engineering, Physics, and Electrical and Computer Engineering, University of Waterloo, Waterloo, ON N2L3G1, Canada
Appl. Sci.2020, 10(22), 8298;https://doi.org/10.3390/app10228298
This article belongs to the Special Issue Medical Digital Image: Technologies and Applications
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Abstract

This paper provides a critical review of the literature on deep learning applications in breast tumor diagnosis using ultrasound and mammography images. It also summarizes recent advances in computer-aided diagnosis/detection (CAD) systems, which make use of new deep learning methods to automatically recognize breast images and improve the accuracy of diagnoses made by radiologists. This review is based upon published literature in the past decade (January 2010–January 2020), where we obtained around 250 research articles, and after an eligibility process, 59 articles were presented in more detail. The main findings in the classification process revealed that new DL-CAD methods are useful and effective screening tools for breast cancer, thus reducing the need for manual feature extraction. The breast tumor research community can utilize this survey as a basis for their current and future studies.

1. Introduction

Due to the anatomy of the human body, women are more vulnerable to breast cancer than men. Breast cancer is one of the leading causes of death for women globally [1,2,3,4] and is a significant public health problem. It occurs due to the uncontrolled growth of breast cells. These cells usually form tumors that can be seen from the breast area via different imaging modalities.
To understand breast cancer, some basic knowledge about the normal structure of the breast is important. Women’s breasts are constructed of lobules, ducts, nipples, and fatty tissues (Figure 1) [5]. Normally, epithelial tumors grow inside the lobes, as well as in the ducts, and later form a lump [6], generating breast cancer.
Figure 1. This scheme represents the anatomy of a woman’s breast. Inside the lobes are the zones where the epithelial tumors or cyst grow. Designed by Biorender (2020). Retrieved from https://app.biorender.com/biorender-templates.
Breast abnormalities that can indicate breast cancer are masses and calcifications [7]. Masses are benign or malignant lumps and can be described in terms of their shape (round, lobular, oval, and irregular) or their margin (obscured, indistinct, and spiculated) characteristics. The spiculated masses are the particular kind of masses that have a high probability of malignancy. A spiculated mass is a lump of tissue with spikes or points on the surface. It is suggestive but not a confirmation of malignancy. It is a common mammography finding in breast carcinoma [8].
On the other hand, microcalcifications are small granular deposits of calcium and may reveal themselves as clusters or patterns (like circles or lines) and appear as bright spots in a mammogram. Benign calcifications are usually larger and coarser with round and smooth contours. Malignant calcifications tend to be numerous, clustered, small, varying in size and shape, angular, and are irregularly shaped [7,9].
Breast cancer screening aims to detect benign or malignant tumors before the symptoms appear, and hence reduce mortality through early intervention [2]. Currently, there are different screening methods, such as mammography [10], magnetic resonance imaging (MRI) [11], ultrasound (US) [12], and computed tomography (CT) [13]. These methods help to visualize hidden diagnostic features. Out of these modalities, ultrasound and mammograms are the most common screening methods for detecting tumors before they become palpable and invasive [2,14,15,16]. Furthermore, they may be utilized effectively to reduce unnecessary biopsies [17]. These two are the modalities that are reviewed in this article.
A drawback in mammography is that the results depend upon the lesion type, the age of the patient, and the breast density [18,19,20,21,22,23,24]. In particular, dense breasts that are “radiographically” hard to see exhibit a low contrast between the cancerous lesions and the background [25,26].
Digital mammography (DM) has some limitations, such as low sensitivity, especially in dense breasts, and therefore other modalities, such as US, are used [12]. US is a non-invasive, non-radioactive, real-time imaging technique that provides high-resolution images [27]. However, all these techniques require manual interpretation by an expert radiologist. Normally, the radiologists try to do a manual interpretation of the medical image via a double mammogram reading to enhance the accuracy of the results [28]. However, this is time-consuming and is highly prone to mistakes [3,29]. Because of these limitations, different artificial intelligence algorithms are gaining attention due to their excellent performance in image recognition tasks.
Different breast image classification methods have been used to assist doctors in reading and interpreting medical images, such as traditional computer-aided diagnosis/detection (CAD) systems [8,30,31,32] based on machine learning (ML) [33,34,35], or based on modern CAD-deep learning (DL) system [36,37,38,39,40,41,42].
The goal of CAD is to increase the accuracy and sensitivity rates to support radiologists in their diagnosis decisions [43,44]. Recently, Gao et al. [45] developed a CAD system for screening mammography readings that demonstrated an approximately 92% accuracy in the classification. Likewise, other studies [46,47] used several CNNs for mass detection in mammography’s and ultrasounds [48,49,50].
In general, DL-CAD systems focus on CNNs, which is the most popular model used for intelligent image analysis and for detecting cancer with good performance [51,52]. With CNNs, it is possible to automate the feature extraction process as an internal part of the network, thus minimizing human interference. DL-CAD systems have added broader meaning with this approach, distinguishing it from traditional CAD methods.
The next-generation technologies based on the DL-CAD system solve problems that are hard to solve with traditional CAD [12,33]. These problems include learning from complex data [53,54], image recognition [55], medical diagnosis [56,57], and image enhancement [58]. In using such techniques, the image analysis includes preprocessing, segmentation (selection of a region of interest—ROI), feature extraction/selection, and classification.
In this review, we summarize recent upgrades and improvements in new DL-CAD systems for breast cancer detection/diagnosis using mammograms and ultrasound imaging and then describe the principal findings in the classification process. The following research questions were used as the guidelines for this article:
  • How the new DL-CAD systems provide breast imaging classification in comparison with the traditional CAD system?
  • Which artificial neural networks implemented in DL-CAD systems give better performance regarding breast tumor classification?
  • Which are the main DL-CAD architectures used for breast tumor diagnosis/detection?
  • What are the performance metrics used for evaluating DL-CAD systems?

2. Materials and Methods

2.1. Flowchart of the Review

The research process is shown in Figure 2, which was in accordance with the PRISMA (Preferred reporting items for systematic reviews and meta-analyses) flow diagram and protocol [59].
Figure 2. PRISMA flow diagram.
Furthermore, the systematic review process follows the flow diagram and protocol (Figure 3) given in [60].
Figure 3. This flowchart diagram represents the review process of articles in this paper. DL-CAD: deep learning computer-aided diagnosis/detection.
We identified appropriate studies in PubMed, Medline, Google Scholar, and Web of Science databases, as well as conference proceedings from IEEE (Institute of Electrical and Electronics Engineers), MICCAI (Medical Image Computing and Computer Assisted Intervention), and SPIE (Society of Photographic Instrumentation Engineers), published between January 2010 and January 2020. The search was designed to identify all studies in which DM and US were evaluated as a primary detection modality for breast cancer, and were both used for screening and diagnosis. A comprehensive search strategy including free text and MeSH terms was utilized, including terms such as: “breast cancer”, “breast tumor”, “breast ultrasound”, “breast diagnostic”, “diagnostic imaging”, “deep learning”, “CAD system”, “convolutional neural network”, “computer-aided detection”, “computer-aided diagnoses”, “digital databases”, “mammography”, “mammary ultrasound”, “radiology information”, and “screening”.

2.1.1. Inclusion Criteria

Articles were included if they assessed computer-aided diagnosis (CADx) and/or computer-aided detection (CADe) for breast cancer, DL in breast imaging, deep CNN, DL in mass segmentation and classification in both DM and US, deep neural network architecture, transfer learning, and feature-based methods regarding automated DM breast density measurements. From a review of the abstracts, we manually selected the relevant papers.

2.1.2. Exclusion Criteria

Articles were excluded if the study population included other screening methods, such as MRI, CT, PET (positron emission tomography), or if other machine learning techniques were used.

2.2. Study Design

The general modern DL-CAD design was divided into four sections (Figure 4). First, different mammography and ultrasound public digital databases were analyzed as input data for the DL-CAD system. The second section includes the preprocessing and postprocessing in the next-generation DL-CAD.
Figure 4. The general diagram is a flowchart that describes how a modern CAD system process can be used with DM and US images from public and private databases. Normally, the CAD system consists of several stages, such as segmentation, feature extraction/selection, and classification. However, DL-CAD systems are based on CNN models and architectures for automatic feature extraction/selection and classification with convolutional and fully connected layers through self-learning. Finally, CAD systems are validated by different metrics. ANN: artificial neural network, BCDR: Breast Cancer Digital Repository, BUSI: Breast Ultrasound Image Dataset, CADe: computer-aided detection, CADx: computer-aided diagnosis, DDBUI: Digital Database for Breast Ultrasound Images, DDSM: Digital Database for Screening Mammography, MIAS: Mammographic Image Analysis Society Digital Mammogram Database, OASBUD: Open Access Series of Breast Ultrasonic Data, ROC–AUC: receiver operating characteristic curve–area under the curve, UDIAT: Ultrasound Diagnostic Ultrasound Centre of the Parc Tauli, VGGNet: Visual Geometry Group.
In the third part, full articles were analyzed to compile the successful CNNs used in DL architectures. Furthermore, the best evaluation metrics were analyzed to measure the accuracy of these algorithms. Finally, a discussion and conclusions about these classifiers are presented.

2.2.1. Public Databases

Normally, DL models are tested using private clinical images or publically available digital databases that are used by researchers in the breast cancer area. The amount of public medical images is increasing because most of the DL-CAD systems require a large amount of data. Thus, DL algorithms are applied to available digitized mammograms, such as those from MIAS (Mammographic Image Analysis Society Digital Mammogram Database) [61], DDSM (Digital Database for Screening Mammography), IRMA (Image Retrieval in Medical Application) [62,63], INbreast [64], and BCDR (Breast Cancer Digital Repository) [45,65], as well as public US databases, such as BUSI (Breast Ultrasound Image Dataset), DDBUI (Digital Database for Breast Ultrasound Images), and OASBUD (Open Access Series of Breast Ultrasonic Data) from the Oncology Institute in Warsaw, Poland, and the private US collected datasets, such as SNUH (Seoul National University Hospital, Korea) [48], Dataset A (collected in 2001 from a professional didactic media file for breast imaging specialists) [66], and Dataset B collected from the UDIAT(Ultrasound Diagnostic Ultrasound Centre of the Parc Tauli) Corporation, Sabadell, Spain. These widely used datasets are listed in Table 1.
Table 1. Summary of the most commonly used public breast cancer databases in the literature.

2.2.2. CAD Focused on DM and US

The CAD systems are divided into two categories. One is the traditional CAD system and the other is the DL-CAD system (Figure 5). In the traditional CAD system, the radiologist or clinician defines features in the image, where there can be problems regarding recognizing the shape and density information of the cancerous area. A DL-CAD system, on the other hand, creates such features by itself through the learning process [74].
Figure 5. The scheme describes the main difference between the traditional machine learning (ML)-CAD system and the DL-CAD system.
Furthermore, CAD systems can be broken down into two main groups: CADe and CADx. The main difference between CADe and CADx is that the first refers to a software tool that assists in ROI segmentation within an image [75], identifying possible abnormalities and leaving the interpretation to the radiologist [8]. On the other hand, CADx serves as a decision aid for radiologists to characterize findings from a CADe system. Several significant CAD works are described in Table 2.
Table 2. The traditional CAD system summary with DM and US breast cancer images. It covers four stages: (i) image processing, (ii) segmentation, (iii) feature extraction and selection, and (iv) classification.

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], 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].
Image Augmentation
Deep CNN depends on large datasets to avoid overfitting and is necessary for good DL model performance [127]. Thus, limited datasets are a major challenge in medical image processing [128] and it is necessary to implement data augmentation techniques. There are two common techniques for increasing the data in DL, namely, data augmentation and transfer learning/fine-tuning [129,130]. Examples of DL models that have been trained with data augmentation are Imagenet [74] and transfer learning [47].
The image augmentation algorithms include basic image manipulations (flipping, rotation, transformation, feature space augmentation, kernel, mixing images, and random erasing [131]) and DL manipulations (generative adversarial networks (GANs)) [132], along with a neural style transfer [133] and meta-learning [128]). These techniques increase the amount of data by preprocessing input image data via operations such as contrast enhancement and noise addition, which have been implemented in many studies [134,135,136,137,138,139,140].
Image Segmentation
This processing step plays an important role in image classification. Segmentation is the separation of ROIs (lesions, masses, and microcalcifications) from the background of the image.
In traditional CAD systems, the tasks of specifying the ROI, such as an initial boundary or lesions, are accomplished with the expertise of radiologists. The traditional segmentation task in DM can be divided into four main classes: (i) threshold-based segmentation, (ii) region-based segmentation, (iii) pixel-based segmentation, and (iv) model-based segmentation [3,78]. Furthermore, US image segmentation includes several techniques: threshold-based, region-based, edge-based, water-based, active-contour-based, and neural-network-learning-based techniques [141,142].
The accuracy of the segmentation affects the results of CAD systems because numerous features are used for distinguishing malignant and benign tumors (texture, contour, and shape of lesions). Thus, the features may only be effectively extracted if the segmentation of tumors is performed with great accuracy [106,142]. This is why researchers are using DL methods, especially CNNs, because these methods have shown excellent results on segmentation tasks. Furthermore, DL-CAD systems are independent of human involvement and are capable of autonomously modeling breast US and DM knowledge using constraints. Two strategies have been utilized for full image sizes for training CNNs for DM and US instead of ROIs: (1) high-resolution [143] and (2) patch-level [144] images. For example, recent network architectures that have been used to produce segmented regions are YOLO [145], SegNet [146,147], UNet [148], GAN [149], and ERFNet [150].

2.2.4. Postprocessing

Image Feature Extraction and Selection
After the segmentation, feature extraction and selection are the next steps to remove the irrelevant and redundant information of the data being processed. Features are characteristics of the ROI taken from the shape and margin of lesions, masses, and calcifications. These features can be categorized into texture and morphologic features [12,86], descriptors, and model-based features [52], which help to discriminate between benign and malignant lesions. Most of the texture features are calculated from the entire image or ROIs using the gray-level value and the morphological features.
There are some traditional techniques used for feature selection, such as searching algorithms, the chi-square test, random forest, gain ratio, and recursive feature elimination [91]. In addition, other traditional techniques used for the feature extraction include principal component analysis (PCA), wavelet packet transform (WPT) [92,93], grey-level co-occurrence matrix (GLCM) [91], Fourier power spectrum (FPS) [94], Gaussian derivative kernels [95], and decision boundary features (DBT) [151].
However, in some classification processes, such as an ANN or support vector machine (SVM), the dimension of the vectors affects both the computational time and the performance [152] because this depends on the number of features extracted. Thus, feature selection techniques reduce the size of the feature space, improving the accuracy and computation time by eliminating redundant features [153]. In particular, DL models produce a set of image features from the data [154], whose main advantage is that they extract features and perform classifications directly. Providing good extraction and selection of the features is a crucial task for DL-CAD systems; for example, some CNNs that are capable of extracting features have been presented by different authors [155,156].

2.2.5. Classification

During the classification process, the dimension of feature vectors is important because these affect the performance of the classifier. The features of breast US images can be divided into four types: texture, morphological, model-based, and descriptor features [86]. After the features have been extracted and selected, they are input into a classifier to categorize the ROI into malignant and benign classes. The commonly used classifiers include linear, ANN, Bayesian neural networks, decision tree, SVM, template matching [106], and CNNs.
Recently, deep CNNs, which are hierarchical architectures trained on large-scale datasets, have shown stunning performances regarding object recognition and detection [157], which suggests that these could also improve breast lesion detection in both US and DM methods. Some researchers are interested in lesion [158], microcalcification [159,160], and mass [161,162] classification in DM and US [15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,163,164,165] images based on CNN models.
Deep Learning Models
DL in medical imaging is mostly represented by a basic structure called a CNN [57,75]. There are different DL techniques, such as GANs, deep autoencoders (DANs), restricted Boltzmann machine (RBM), stacked autoencoders (SAEs), convolutional autoencoders (CAEs), recurrent neural networks (RNNs), long short-term memory (LSTM), multiscale convolutional neural network (M-CNN), and multi-instance learning convolutional neural network (MIL-CNN) [3]. DL techniques have been implemented to train neural networks for breast lesion detection, including ensemble [75] and transfer learning [129,157,166] methods. The ensemble method combines several basic models in order to get an optimal model [167], and transfer learning is an effective DL method to pre-train models to deal with small datasets, as in the case of medical images.
ANNs are composed of an input and output layer, plus one or more hidden layers, as shown in Figure 6. In the field of breast cancer, three types of ANN are frequently used: backpropagation, SOM, and hierarchical ANNs. To train an ANN with a backpropagation algorithm, the error function is given to calculate the gradient descent. This error propagates in the backward direction and the weights are adjusted for error reduction. This processing is repeated until the error becomes zero or is a minimum [3].
Figure 6. An ANN learns by processing images, where each of which contains an input, hidden, and result layer.
Convolutional Neural Networks
CNNs are the most widely used Neural Networks when it comes to DL and medical image analysis. The CNN structure has three types of layers: (i) convolution, (ii) pooling, and (iii) full-connection layers, which are stacked in multiple layers [74]. Thus, a CNN’s structure is determined by different parameters, such as the number of hidden layers, the learning rate, the activation function (rectified linear unit (ReLU)), pooling layer for feature map extraction, loss function (softmax), and the fully connected layers for classification, as shown in Figure 7.
Figure 7. A feed-forward CNN network, where the convolutional layers are the main components, followed by a nonlinear layer (rectified linear unit (ReLU)), pooling layer for feature map extraction, loss function (softmax), and the fully connected layers for classification. The output can be either benign or malignant classes.
Furthermore, there are several methods for improving a CNN’s performance, such as dropout and batch normalization. Dropout is a regularization method that is used to prevent a CNN model from overfitting. A batch normalization layer speeds up the training of CNNs and reduces the sensitivity to network initialization.

2.2.6. Evaluation Metrics

Different quantitative metrics are used to evaluate the classifier performance of a DL-CAD system. These include accuracy (Acc), Sensitivity (Sen), Specificity (Spe), area under the curve (AUC), F1 score, and a confusion matrix. The statistical equations are shown in Table 3 and Table 4.
Table 3. Confusion matrix for a binary classifier that is used to distinguish between two classes, namely, benign and malignant. TP: true positive; FN: false negative, FP: false positive, TN: true negative, TPR: true positive rate, FPR: false positive rate.
Table 4. Validation assessment measures.
The receiver operating characteristic curve (ROC) is a graph for plotting the true positive rate (TPR) versus a false positive rate (FPR) and is derived from the AUC. The TPR and the FPR are also called sensitivity (recall) and specificity, respectively, as shown in Figure 8.
Figure 8. The confusion matrix for the ROC. The number of images correctly predicted by the classifier is located on the diagonal. The ROC curve utilizes the TPR on the y-axis and the FPR on the x-axis.
The AUC provides the area under the ROC curve and a perfect score has a range from 0.5 to 1. A 100% correct classified version will have an AUC value of 1 and it will be 0 if there is a 100% wrong classification [168].
Cross-validation is a statistical technique that is used to evaluate predictive models by partitioning the original samples into training, validation, and testing sets. There are three types of validation: (1) hold-out splits (training 80% and testing 20%), (2) three-way data split (training 60%, validation 20%, and testing 20%), and (3) K-fold cross-validation (from 3 to 5 k-fold for a large data set, 10 k-fold for a small dataset), where the data are split into k different subsets depending on their size [65].

3. Results

3.1. CNN Architectures

A model’s performance depends on the architecture and the size of the data. In this sense, there are different CNN architectures that have been proposed: AlexNet [169], VGG-16 [170], ResNet [171], Inception (GoogleNet) [172], and DenseNet [173]. These networks have shown promising performance in recent works for image detection and classification. Table 5 shows a brief description of these networks.
Table 5. Summary of CNN architecture information for breast imaging processing.

3.2. Performance Metrics

Furthermore, brief reviews of the DL architectures based on DM and US breast images, along with their evaluation metrics, are presented in Table 6 and Table 7 [50,180].
Table 6. The quantitative indicators that were used to evaluate the performance between different CNN architectures in DM datasets.
Table 7. The quantitative indicators that were used to evaluate different CNN architectures’ performances on US datasets.
Furthermore, Table 8 gives a brief overview of the new DL-CAD systems’ approaches and the traditional ML-CAD systems.
Table 8. DL-CAD systems vs. traditional ML-CAD systems.

4. Discussion and Conclusions

Considering that breast tumor screening using DM has some consequences and limitations because a higher number of unnecessary biopsies and ionizing radiation exposure endangers the patient’s health [12], along with low specificity and high FP results, which imply higher, recall rates and higher FN results [191]. This is why US is used as the second choice for DM. Thus, US imaging is one of the most effective tools in breast cancer detection because it has been shown to achieve high accuracy in mass detection, classification [38], and diagnosis of abnormalities in dense breasts [192].
For the abovementioned reasons, we have addressed using both kinds (DM and US) of images in this review, focusing on different ML and DL architectures applied in breast tumor processing, and offering a general overview of databases and CNNs, including their relation and efficacy in performing segmentation, feature extraction, selection, and classification tasks [192].
Thus, according to the research shown in Table 1, the most utilized databases for DM images are MIAS and DDSM, and for US image classification, the public databases BUSI, DDBUI, and OASBUD are most used. The DM images contributed to 110 and 168 published conference papers for the DDSM and MIAS databases, respectively [5]. However, the databases report some limitations and advantages; for example, the MIAS database contains a limited number of images, strong noise, and low-resolution images. In contrast, the DDSM contains a big dataset. Likewise, INbreast contains high-resolution images but has a small data size. BCDR, in comparison with DDSM, has been used in a few studies. Some details about the others strengths and limitations of these databases are discussed in Abdelhafiz [65].
Thereby, Table 2 shows a summary of traditional ML-CAD systems that use public and private databases of DM and US breast images. It covers (i) image preprocessing and (ii) postprocessing steps. This is in contrast with Table 5, which shows a brief summary of DL-CAD systems based on CNN architectures in both types of digital breast images. Thus, in Table 5, various DL architectures and their training strategies for detection and classification tasks are discussed. Based on the most popular datasets, CNN seems to perform rather well, as demonstrated by Chiao et al., Yap et al., and Samala et al. [48,153,174]. Furthermore, [169,173] used several preprocessing and postprocessing techniques for high-resolution [58] data augmentation, segmentation, and classification. The most commonly CNNs used are AlexNet, VGG, ResNet, DenseNet, Inception (GoogleNet), LeNet, and UNet, which employ recent Python libraries for implementing CNNs, such as Tensorflow, Caffe, and Keras, with different hyper-parameters to training the network [55].
Most of these DL architectures use a large data set; thus, it is required to apply an augmentation technique to avoid overfiting and to have better performance during classification. In this sense, the researchers mentioned in Table 6 [145,168,180,181] and Table 7 [35,49,62,66,152,182] the authors used transfer learning and ensemble methods, such as data augmentation, to improve the performance of the CNN network, reaching an 89.86% accuracy and 0.9578% AUC in DM, and an AUC of 0.68% on US images. Furthermore, Singh et al. [165] showed that the results obtained with a GAN for breast tumor segmentation outperformed the UNet model, and the SegNet and ERFNet models yielded the worst segmentation results on US images.
In addition, according to Cheng et al. [37], DL techniques could potentially change the design paradigm of CADx systems due to their several advantages over the traditional CAD systems. These are as follows: First, DL can directly extract features from the training data. Second, the feature selection process will be significantly simplified. Third, the three steps of feature extraction, selection, and classification can be realized within the same deep architecture. Thus, SDAE architecture can potentially address the issues of high variation in either the shape or appearance of lesions/tumors. Furthermore, various studies [39,40,41,55] prove that those CNN methods that compare images from CC and MLO views improve the accuracy of detection and reduce the FPR.
Furthermore, different evaluation metrics are described in Table 3 and Table 4 as corroboration of the performance of these techniques. The results in Table 6 and Table 7 describe different research where their authors have used a variety of datasets (Table 1), approaches, and performance metrics to evaluate CNN techniques in DM and US imaging. For example, better results were achieved in DM analysis by Al-Masni [145] with YOLO5 using DDSM data augmentation, while Chougrad et al. [181] used a deep CNN (Inception V3) with DDSM and MIAS datasets. On the other hand, Moon et al. [49] introduced a DenseNet model to analyze private (BUSI and SNUH) US datasets. Byra et al. [66] achieved high accuracy with the VGG19 deep CNN model using the ImageNet database. Similarly, Cao et al. [152] attained an accuracy of 96.89% with SSD + ZFNet and Han et al. [62] reached 91.23% using a private dataset with GoogleNet.
Likewise, Table 8 contains a literature review for the comparison of the evaluation metrics between DL-CAD systems and traditional ML-CAD systems. Even though Table 8 shows that Deheeba et al. [183] presented a good traditional wavelet neural network CAD system with high accuracy (93.67%) and AUC of 96.85%, Debelee et al. [42] exceeded this percentage using a CNN + SVM DL-CAD system with DDSM (99%) and MIAS (97.18%) DM datasets. In US images Zhang et al. [36] and Shi et al. [190] proved that a DL-CAD based on CNN and a deep polynomial network achieved better results in terms of accuracy (93.4 and 92.40%) and AUC (94.7%), respectively. In the same way, DL-CAD reached higher values than ML-CAD when used on private US images. For example, Shan et al. [35] and Singh et al. [41] showed ML based on an ANN for segmentation and classification that reached accuracies of 78.5 and 95.86% and an AUC of 82%, respectively. These works demonstrate that in most cases, the DL architectures outperformed traditional methodologies.
To conclude, the use of DL could be a promising new technique to obtain the main features for automatic breast tumor classification, especially in dense breasts. Furthermore, in medical image analysis, using DL has proven to be better for researchers compared to a conventional ML approach [41,42]. It appears as though DL provides a mechanism to extract features automatically through a self-learning network, thus boosting the classification accuracy. However, there is a continuing need for better architectures, more extensive datasets that overcome class imbalance problems, and better optimization methods.
Finally, the main limitation in this work is that several algorithms and results are not available in the open literature because of proprietary intellectual property issues.

Author Contributions

Conceptualization, Y.J.-G. and V.L.; methodology, Y.J.-G.; formal analysis, Y.J.-G., M.J.R.-Á., and V.L.; investigation, Y.J.-G.; resources, Y.J.-G.; writing—original draft preparation, Y.J.-G.; writing—review and editing, Y.J.-G., M.J.R.-Á., and V.L.; visualization, Y.J.-G.; supervision, M.J.R.-Á. and V.L.; project administration, M.J.R.-Á. and V.L.; funding acquisition, V.L. All authors have read and agreed to the published version of the manuscript.

Funding

This project has been co-financed by the Spanish Government Grant PID2019-107790RB-C22, “Software development for a continuous PET crystal systems applied to breast cancer”.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

ANNartificial neural network
CADxcomputer-aided diagnosis
CADecomputer-aided detection
CNNconvolutional neural network
DMdigital mammography
DLdeep learning
DNNdeep neural network
DL-CADdeep learning CAD system
CCcraniocaudal
MCmicrocalcifications
MLmachine learning
MLOmediolateral oblique
ROIregion of interest
USultrasound
MLPMuli-layer perceptron
DBTdigital breast tomosynthesis
MILmultiple instances learning
CRFconditional random forest
RPNregion proposal network
GANgenerative adversarial network
IoUintersection over union
SDAEstacked denoising auto-encoder
CBISCurated Breast Imaging Subset
YOLOYou Only Look Once
ERFNetEfficient Residual Factorized Network
CLAHEcontrast-limited adaptive histogram equalization
PCAprincipal component analysis
LDAlinear discriminant analysis
GLCMgrey-level co-occurrence matrix
RFrandom forest
DBTdecision boundary features
SVMsupport vector machine
NNneural network
SOMself-organizing map
KNNK-nearest neighbor
BDTbinary decision tree
DBNdeep belief networks
WPTwavelet packet transform

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