Deep learning algorithm for breast masses classification in mammograms

IET Image ProcessingVolume 14, Issue 12 p. 2860-2868 Research ArticleFree Access Deep learning algorithm for breast masses classification in mammograms Vaira Suganthi Gnanasekaran, Corresponding Author Vaira Suganthi Gnanasekaran vairasuganthi@gmail.com Sethu Institute of Technology, Pulloor, Kariapatti, Virudhunagar District, Tamilnadu, IndiaSearch for more papers by this authorSutha Joypaul, Sutha Joypaul AAA College of Engineering and Technology, Sivakasi, Tamilnadu, IndiaSearch for more papers by this authorParvathy Meenakshi Sundaram, Parvathy Meenakshi Sundaram Sethu Institute of Technology, Pulloor, Kariapatti, Virudhunagar District, Tamilnadu, IndiaSearch for more papers by this authorDurga Devi Chairman, Durga Devi Chairman Madurai Medical College, Madurai, Tamilnadu, IndiaSearch for more papers by this author Vaira Suganthi Gnanasekaran, Corresponding Author Vaira Suganthi Gnanasekaran vairasuganthi@gmail.com Sethu Institute of Technology, Pulloor, Kariapatti, Virudhunagar District, Tamilnadu, IndiaSearch for more papers by this authorSutha Joypaul, Sutha Joypaul AAA College of Engineering and Technology, Sivakasi, Tamilnadu, IndiaSearch for more papers by this authorParvathy Meenakshi Sundaram, Parvathy Meenakshi Sundaram Sethu Institute of Technology, Pulloor, Kariapatti, Virudhunagar District, Tamilnadu, IndiaSearch for more papers by this authorDurga Devi Chairman, Durga Devi Chairman Madurai Medical College, Madurai, Tamilnadu, IndiaSearch for more papers by this author First published: 08 September 2020 https://doi.org/10.1049/iet-ipr.2020.0070Citations: 3AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract A mammogram is an image of a breast used to detect and diagnose breast cancer. This paper emphases a Computer-Aided Detection system based on convolutional neural network (CNN) that uses the concept of deep learning to classify the mammogram images into benign, malignant and normal. The proposed CNN model consists of eight convolutional, four max-pooling and two fully connected layers and achieved better results compared to the pre-trained nets, AlexNet and VGG16. The proposed model demonstrates the feasibility of using CNNs on medical image processing techniques for the classification of breast masses. The results are also compared with the state-of-the-art machine learning algorithm like kNN classifier. Experimentation is done with three datasets. Among them, two are publicly available, Mammographic Image Analysis Society (MIAS), digital database for screening mammography (DDSM) and an internally collected dataset. The proposed model achieved accuracies of 92.54, 96.47 and 95 and the Area under the ROC curve (AUC) score of 0.85, 0.96 and 0.94 for MIAS, DDSM and the internally collected dataset respectively. Furthermore, the images of the three datasets are merged to build one large set and used to fine tune the proposed CNN model and produced accuracy of 98.32 and AUC of 0.98. 1 Introduction Breast cancer is the major cause of cancer deaths among women all over the world. The number of deaths can be reduced by detecting it at its early stages. There are effective techniques available to find out the presence of cancer. The chance of getting cured is high if the disease is identified at its early stages. There are several modalities available for taking the images of breasts such as Mammography, Tomosynthesis, Molecular Breast Imaging, and Magnetic Resonance Imaging. Among them, the most widely used imaging technique is mammography because of its low cost and availability [1]. Mammography is the traditional method used by many people. It uses low-dose X-rays to inspect the human breast. It is a very simple approach and can be easily affordable by people. Cancerous masses and calcium deposits look brighter on the mammogram. Currently, mammography is the gold standard method to detect early stage breast cancer before the lesions become clinically palpable. It has helped to decrease the mortality rate by 25 to 30%. Radiologists take two views of each breast hence two images are produced. They are MLO (Medio Lateral Oblique) view and CC (Cranio Caudal) view. Since the 1980s, numerous research works with mammography images have been conducted. As there has been a tremendous increase in the number of image processing algorithms, this research field is ever-evolving. Artificial Intelligence and Machine Learning techniques have begun to prove their importance in image processing, i.e. classification of breast masses, micro-calcifications, architectural distortions, and bilateral asymmetry [2]. Research work these days focus mainly on deep learning algorithms. Deep learning techniques refer to a range of Artificial Neural Networks comprising of multiple layers. The techniques mimic the human brain's structure and cognitive behaviour. Deep learning relies not on the hand-crafted features, instead acquire knowledge directly from the data. Deep learning gets raw data as inputs, learns features by itself and classifies the inputs into class labels. CNN is a special type of multi-layer feed forward neural network that is best suited for classification. Each neuron takes inputs from a local receptive field in the previous layer, and forces it to extract local features. Each convolution layer is composed of multiple feature maps and each one is in the form of a plane within which the individual neurons are constrained. A convolutional neural network (CNN) is a deep artificial neural network which is used mainly to classify images. The usefulness of convolutional nets (CNNs) in image processing has increased the effectiveness of deep learning. Especially, in the field of Medical Image Processing, deep learning plays a vital role [3]. The aim of this paper is to develop a Computer-Aided Detection system methodology to help the radiologists to classify mass lesions, based on CNN with a limited number of layers without losing features for getting a good prediction and also is scalable to massive datasets. The paper is organised as follows: after reviewing a fraction of the existing literature in Section 2, the proposed model is discussed in Section 3. Section 4 details the results and discussion on various parameters used for the comparison. Section 5 concludes the proposed work. 2 Related works Methods for classifying breast masses in mammograms have been a continuous topic in the literature for many years, due to its role in image processing. In recent years, machine learning and deep learning algorithms play a vital role in the research. Litjens et al. [4] reviewed the most important deep learning concepts related to medical image analysis and summarised more than 300 research papers in the field. Their survey focused on the application of deep learning algorithms for various image processing tasks. They considered various studies per application area, which were neuro, retinal, pulmonary, digital pathology, breast, cardiac, abdominal etc. Neural networks are a type of learning algorithm which forms the basis of most deep learning methods. An improved dense net neural network model is proposed by Li et al. [5]. In this model, the first convolution layer is replaced with an inception structure. The model used 40 layers including 3 dense block layers and 2 transition layers. The accuracy of the model is 94.55%. Multi U-net algorithm which is based on CNN proposed by Viksit Kumar et al. (2018) [3] used to segment breast masses from clinical ultrasound scans. The U-net algorithm has two branches, encoding on the left side and decoding on the right side. Encoding branch extracted the features from the input image after going through many convolutions and the decoding branch rebuilt the image. The number of images used for the experimentation is limited and methodology is used only for segmenting the masses, not for classification. The mammogram images are divided into various patches before given to convolution layers. A method which used patches based on texture-based classification of masses in mammograms did not use any segmentation of the mass region. Rather, patches from filter bank responses are used for generating the texton dictionary. The authors used the digital database for screening mammography (DDSM) database for evaluation. Naive Bayes classifier is used for classification and produced an accuracy of 83% and 0.89 for Area under the ROC curve (AUC) Suhail et al [6]. Mohamed et al. [7] developed an approach to distinguish between the two BI-RADS density categories, scattered heterogeneously. To achieve the highest accuracy they used a deep learning-based CNN framework with more than 22,000 images. The proposed CNN architecture consisted of five convolutional, three max-pooling and three fully connected layers. Fully connected layers use 2-way softmax function. The output of the net was used to classify breast density. Their future work is to develop a method for breast mass classification. A combination of the Dense CNN (DenseNet) and the Squeeze-and-Excitation (SE) block named SEDenseNet, and the twice fine-tuning method proposed by Cai et al. [8] to classify breast mass. For the initial tuning of the model, the mass and normal breast are trained on ImageNet. Later breast mass data set including benign and malignant images for further fine-tuning the model. A large number of experiments on the BCDR data set and quantitative comparison with other methods is done to verify the correctness of the model. The model achieved an accuracy of 98.2% and Area Under the Curve (AUC) of 0.984. Ahmed and Salem [9] used the concept of a multi-view CNN, a 6-input CNN taking mass region, Micro-calcifications region for both MLO and CC views of each breast along with the views. The six CNNs are merged to give a concatenated vector, to train a binary classifier. For experimental evaluation, INbreast data set is used and produced an accuracy of 80.10% and the AUC score was 0.78. Ahmed and Salem [9] used a CNN to classify images of the Mammographic Image Analysis Society (MIAS) dataset. Watershed segmentation algorithm is used to pre-process the images. The algorithm is compared with the traditional machine learning algorithms like support vector machine, random forest and produced an accuracy of 98%. Yemini et al. [10] briefed that most of the deep neural networks, trained on natural images, exhibit a common phenomenon. On the first layer, they learn. Such first-layer features appear not too specific to a particular data set. They are applicable to many data sets. These first-layer features are called general since finding the standard features on the first layer seems to occur irrespective of the exact cost function and natural image data set. In a network with an N-dimensional softmax output layer that has been successfully trained towards a supervised classification, each output unit will be specific to a particular class. Hence the last-layer features are called specific. In transfer learning, a base network on a base data set is trained first, and then the learned features are transferred to a second target network to be trained on a target data set. This process works well if the features are general, that is, suitable for both base and target tasks. Lo et al. [11] presented an architecture based on the concept of convolution neural network. The architecture was named as multiple circular path convolution neural networks which analysed tumour and tumour-like structures. The extracted region of interest was divided into sectors with equal angles. The features were extracted from the sector and given as input values for the input layer. Then they are coordinated by a number of convolutional layers. 3 Proposed CNN model The approach used in this paper shows the huge power of neural networks for a scalable dataset. Three different databases of mammogram images are used to perform learning and to benchmark the CNN model. The three datasets are merged to boost the learning process. The merged dataset is referred to as CD data set and the internally collected data set as ID dataset. Fig. 1, illustrates the two approaches used in this work for breast masses classification in mammograms. For both machine learning and deep learning approaches, the same sets of mammogram images are used. Fig. 1Open in figure viewerPowerPoint General flow diagram The initial step is the pre-processing of images. This is a very important task in all image processing applications. The overall flow of the proposed work is explained in Fig. 1. As mentioned in the previous work, [12] the steps followed in breast masses classification in mammograms using a machine learning algorithm are also explained in Fig. 1. 3.1 Breast mass classification using machine learning algorithm Step 1. Pre-processing: As a first step in the classification technique, an adaptive histogram equalisation algorithm is applied to the mammogram for enhancing the image. CLAHE algorithm works on small regions of the grey image by dividing the image into contextual regions known as tiles. Each tiles contrast is improved by applying histogram equalisation [13]. Since the images are taken using various instruments, they may suffer from the poor contrast and there may be artefacts in the images [14]. The images of both CC and MLO views are used in this work. MLO view of the image contains pectoral muscle. The separation of pectoral muscle improves the classification rate [15]. In MLO view of the image the pectoral muscle is visible at the top left corner if the image is left oriented. If the image is right oriented, it is flipped so that the pectoral muscle comes at the top left corner. To make this muscle clearer, contrast enhancement technique is applied to the image. Using top hat morphological operation, the labels shown in the image are removed. Using intensity-based approach the pectoral muscle is removed from the image. In Fig. 2, the steps involved in the pre-processing are explained with an image taken from the MIAS database. Fig. 2Open in figure viewerPowerPoint Example mammogram image after pectoral muscle removal Step 2. The region growing algorithm is applied to segment the ROI. This approach is a luminance-based approach used for image segmentation [16]. The algorithm starts with a seed pixel which is chosen manually inside the suspicious region, and then, the area around the seed pixel is expanded to extend the region, which includes the neighbouring pixels falling within a threshold range [17]. Step 3. After segmenting the ROI, a set of 14 textural features and 5 geometric features are computed from the ROI: Kurtosis, Dispersion, Median, Variance, Skewness, Mean, Energy, Entropy, Correlation, Contrast, Homogeneity, Auto Correlation, and Cluster Prominence, and Cluster Shade. And the five geometric features are the area of the ROI, perimeter, eccentricity, circularity, and rectangularity [17]. Step 4. From the feature vector with 14 features, an optimum set of features is selected with the help of a genetic algorithm. A typical genetic algorithm requires the following two functions. They are (i) a genetic representation of the solution domain; and (ii) a fitness function to evaluate the solution domain. After defining these functions, GA can continue to initialise a population of solutions randomly, then it can be improved by continual application of various GA operators like selection, crossover, and mutation [18, 19]. Step 5. Image classification is the process of analysing the numerical properties of various image features and organises data into categories [20]. A kNN classifier is involved in the classification of mammograms into normal versus abnormal. 3.2 About deep convolutional neural network CNN processes images as tensors. Tensors are matrices of numbers with additional dimensions. Tensors are formed by arrays nested within arrays, and this testing goes indefinitely [21]. A CNN does not perceive images as humans do. Convolutional layers, pooling layers, and fully connected layers are three important layers of a CNN. Each layer has a different functionality. CNNs are basically layers of convolutions followed by sub-sampling and fully connected layers [22]. Convolutions and sub-sampling layers work as feature extraction layers. The fully connected layer is used to classify the category of current input using extracted features. The task of a pooling layer is to minimise the dimensions of feature maps and network parameters. Convolution preserves the relationship between pixels by learning image features using small squares of input data. It is a mathematical operation that takes two inputs such as image matrix and a filter or kernel [23]. It includes convolutional filters. These filters compute the convolution result of the input feature map with trainable 2D discrete convolution filters and bias parameters. Each filter bank detects a particular feature at each location on the input map [24]. In this paper, all the input images are resized to 256 256. After applying convolution, the output image matrix is given as input to a max-pooling layer. This unit performs down-sampling for the spatial dimension of the input and produces a reduced-resolution output feature map. This feature map is healthy to small variations in the location of features in the previous layer. In addition, it merges semantically similar features into one [25]. Activation function is a non-linear element-wise operator used for simulating the excitability of neurons. Among various activation functions like tanh, Sigmoid and Rectified Linear Unit (ReLU) in deep learning, the ReLU is used in this paper [26]. Normalisation layer is used to acquire an improved description of the input that can be implemented at each spatial location across all feature maps of the same layer [27]. 3.3 Detailed architecture of the proposed CNN model The rapid development of computing power for the application of big data shows its improvement using deep learning in the fields of medical image processing, satellite image processing, natural language processing etc. [28]. Therefore, the use deep learning algorithms for automatic classification of breast masses is an important research area for the detection and diagnosis of breast tumours. By constructing a CNN with multiple hidden layers, the low-level features are transformed by non-linear features layer-by-layer to arrive at a high-level feature expression for the representation of data. The most commonly used deep learning models, CNNs use 2D or 3D images as input, avoid the feature extraction methods as in conventional machine learning models [29]. The proposed CNN model used in this paper consists of four blocks of layers. Each block has two consecutive convolution layers with a varying number of filters, a max pool layer and a drop out layer. The output from the final block is given to the fully connected, softmax layer. This layer is used to classify the images into benign, malignant, and normal. When input images are fed into the CNN, it learns the features from the images; based on the learnt features it classifies the images [30]. Fig. 3 shows the detailed architecture of the CNN model. Fig. 3Open in figure viewerPowerPoint Detailed architecture of the proposed model The advantages of the proposed model are focused in the following aspects: (1) Reduced number of layers. (2) Reduced number of parameters. (3) Strengthens the feature transmission. First, an experimentation is carried out to fine tune the number of layers in the proposed model. Initially, the model is tested with only three blocks of layers. Then the number of blocks is increased to four. The accuracy obtained with four blocks is better than three blocks. Later, the number of blocks is increased to five and it is noticed that the accuracy slightly decreased. Hence, the proposed architecture in Fig. 3 is fixed for further evaluation. Table 1 describes the layers description for the proposed model. Table 1. Layers' description of the proposed model Layer's Name Type / Stride Filter Activation Input Image Input — 256 256 3 C1 Convolution / s1 3 3 3 32 254 254 32 C2 Convolution / s1 3 3 3 32 252 252 32 P1 MaxPool / s2 — 126 126 32 D1 dropout — 126 126 32 C3 Convolution / s1 3 3 3 32 124 124 32 C4 Convolution / s1 3 3 3 32 122 122 32 P2 MaxPool / s2 — 61 61 32 D2 dropout — 61 61 32 C5 Convolution / s1 3 3 3 64 59 59 64 C6 Convolution / s1 3 3 3 64 57 57 64 P3 MaxPool / s2 — 28 28 64 D3 dropout — 28 28 64 C7 Convolution / s1 3 3 3 64 26 26 64 C8 Convolution / s1 3 3 3 64 24 24 64 P4 MaxPool / s2 — 12 12 64 D4 dropout — 12 12 64 FC1 Fully Connected 256 9216 1 1 256 FC2 Fully Connected 3 256 1 1 3 ReLUs are applied for the hidden layers. If the input is less than zero, the ReLU has output zero. If the input is greater than zero, then the output is equal to input. ReLU activation function is shown in (1) (1)Final layer in the architecture is fully connected layer, is also defined as the classification layer. This layer produces the 3-class output [31]. 3.4 Data sets MIAS [32] the Mammographic Image Analysis Society is an organisation of UK research groups that has generated a database of digital mammograms for the research community. The database contains 322 digitised films including radiologist's truth-markings on the locations of any abnormalities that may be present. Among these 322 images, 208 are normal, 68 are benign and 54 are malignant. Radiologists detailed the abnormalities of these images; micro-calcification (30 cases), architectural distortion (19 cases), circumscribed masses (25 cases), speculated masses (15 cases) and bilateral asymmetry (15 cases). The database has been reduced to a 200 micron pixel edge and clipped so that all the images are (Fig. 4). Fig. 4Open in figure viewerPowerPoint Benign, malignant, normal images from MIAS database DDSM [32] the Digital Database for Screening Mammography is a collaborative effort between Massachusetts General Hospital, Sandia National Laboratories and the University of South Florida Computer Science and Engineering Department. Images containing suspicious areas have associated pixel-level 'ground truth' information about the locations and types of suspicious regions. A total of 1416 mammogram images with only breast masses from this data set including 287 normal cases, 541 benign cases and 588 malignant cases is collected from this database (Fig. 5). Fig. 5Open in figure viewerPowerPoint Benign, malignant, normal images from DDSM database ID the internally collected data set has 202 images with ground truth given by a radiologist in India. Among them, 80 are normal cases, 62 are benign cases and 60 are malignant cases (Fig. 6). Fig. 6Open in figure viewerPowerPoint Benign, malignant, normal images from ID database The images of the three data sets are stored in three different folders. To execute the model, a split of 80% of the images are used for training/validation and 20% of the images are used for testing. 3.5 Evaluation To run the CNNs, MATLAB 2019a version on an Intel i7 processor with GPU support is used. The proposed model accepts images of size 256 256. The model is executed with the three data sets separately. To evaluate the CNNs, stratified 5-fold cross-validation is used. The mean accuracy, standard deviation and time elapsed for each model are recorded and are used for comparing the different architectures. To examine the performance of the proposed model on DDSM, MIAS, ID and MD data sets, the blocks of the model were fine tuned for 200 epochs with a batch size of 32 images along with the dropout rate of 0.25 and learning rate of 0.001. To compare the results of the proposed model, with the same data sets, the pre-trained AlexNet, VGG16 and machine learning algorithm are executed. 4 Results and discussion For the experimental evaluation, the proposed method is compared with the pre-trained deep learning models, AlexNet, VGG16 and machine learning algorithm. AlexNet accepts images of size 227 227 and VGG16 accepts images of size 224 224. AlexNet, Guo et al. [33] introduced over a decade later, was relatively shallow, consisting of five convolutional layers, and employed kernels with large receptive fields in layers close to the input and smaller kernels closer to the output. AlexNet included rectified linear units as the activation function. VGG16 is a CNN model proposed by Simonyan and Zisserman. Compared to AlexNet, VGG16 is very slower in execution [34]. To evaluate the model, four metrics are used: classification accuracy and AUC (Area Under Receiver Operating curve), recall, and precision. Using the confusion matrix given in Table 2, the terms accuracy, recall, and precision are calculated as Table 2. Confusion Matrix Benign (B) Malignant (M) Normal (N) Benign (B) Malignant (M) Normal (N) (2)where TOT refers to the total number of images. (3) (4) (5) (6) (7) (8) Table 3 shows the summary of the results obtained by executing the CNNs with the four databases. To evaluate the performance of the model, the input images are stored in three different folders with the names, 'Benign', 'Malignant', and 'Normal'. The images are pre-processed and given to the model. The model randomly selected 80% of the images as training images and 20% of the images as test images. The softmax function gave normalised class probabilities for the output being 'Benign', 'Malignant' or 'Normal'. Table 3. Summary of the results Dataset Number Model Training Testing std, % Time of Images Accuracy Accuracy elapsed (Min) ID 202 AlexNet 74.07 72.5 29 VGG16 71.6 67.5 496 Proposed 90.74 95 35 MIAS 322 AlexNet 76.81 49.25 32 VGG16 48.67 35 594 Proposed 91.63 92.54 38 DDSM 1416 AlexNet 90.47 91.87 151 VGG16 91.08 93.64 960 Proposed 94.35 96.47 610 MD 1940 AlexNet 94.29 95.12 223 VGG16 95.76 96.4 1743 Proposed 97.75 98.32 682 Training and testing accuracy of the proposed CNN for the four data sets are highlighted in bold. Table 4 reports the performance of each model based on the class-wise training and testing accuracy. The proposed model produced higher accuracy for the four data sets. As a matter of fact, with the limited number of layers, the model achieved good results. The results of the merged data set show that the performance is improved when provided with large data. The performance with MIAS data set compared with DDSM and MD data sets reports that while the architecture of the model is essential, the quality and quantity of the training data set is also important. Table 4. Accuracy per class Benign Malignant Normal Accuracy Accuracy Accuracy Dataset Model Train Test Train Test Train Test ID AlexNet 68 58.33 75 66.67 78.13 87.5 VGG16 66 66.67 70.83 58.33 76.56 75 Proposed 90 91.67 91.67 91.67 90.63 100 MIAS AlexNet 68.52 50 81.4 54.55 78.31 47.62 VGG16 37.04 14.29 48.89 18.18 51.81 23.81 Proposed 88.89 85.71 93.02 90.91 92.17 95.24 DDSM AlexNet 90.87 91.23 94.69 91.67 95.74 92.37 VGG16 86.52 94.74 91.92 93.52 91.06 93.22 Proposed 90.87 94.74 94.69 96.3 95.74 97.46 MD AlexNet 92.51 90.36 92.94 94.66 96.15 97.71 VGG16 94.83 92.77 96.72 96.95 97 97.71 Proposed 96.11 96.39 97.71 98.47 98.57 98.86 4.1 Performance of proposed model Due to the limited number of training samples, to prevent the model from overfitting, the weights of the model are applied an L2 regularisation with smaller values and a drop out layer is added. Table 5 shows the calculated precision and recall for the three classes: Benign, Malignant and Normal. The values evaluated by the proposed model is higher than that of the pre-trained models. Table 6 gives the AUC values for the pre-trained models, proposed model and the machine learning model. This table also gives a good impact on the proposed model. Table 5. Recall and Precision per class Benign Malignant Normal Dataset Model Recall Precision Recall Precision Recall Precision ID AlexNet 0.58 0.7 0.67 0.67 0.88 0.78 VGG16 0.67 0.62 0.58 0.7 0.75 0.71 Proposed 0.92 0.92 0.92 1 1 0.94 MIAS AlexNet 0.5 0.33 0.55 0.3 0.48 0.77 VGG16 0.14 0.09 0.18 0.09 0.24 0.45 Proposed 0.86 0.86 0.91 0.83 0.95 0.98 DDSM AlexNet 0.91 0.87 0.92 0.93 0.92 0.94 VGG16 0.95 0.89 0.94 0.94 0.93 0.96 Proposed 0.95 0.95 0.96 0.96 0.97 0.97 MD AlexNet 0.9 0.91 0.95 0.97 0.98 0.96 VGG16 0.93 0.95 0.97 0.96 0.98 0.97 Proposed 0.96 0.98 0.98 0.98 0.99 0.99 Table 6. AUC Model AlexNet VGG16 kNN Proposed CNN Model ID 0.71 0.65 0.9 0.94 MIAS 0.51 0.24 0.81 0.85 DDSM 0.92 0.94 0.92 0.96 MD 0.95 0.965 0.95 0.98 AUC values of the CNN model for various data sets are highlighted bold. Fig. 7 shows the plot of accuracy and loss over the 200 epochs for the proposed model trained on the merged data set, termed as MD. Figs. 8, 9–10 depict the ROC curve of the proposed CNN with the three data sets, MIAS, DDSM, and ID. Fig. 7Open in figure viewerPowerPoint Plot of accuracy and loss over the epochs for the proposed model trained on the merged data set Fig. 8Open i