Novel breast cancer classification framework based on deep learning

IET Image ProcessingVolume 14, Issue 13 p. 3254-3259 Research ArticleFree Access Novel breast cancer classification framework based on deep learning Wessam M. Salama, Corresponding Author Wessam M. Salama wessam.salama@pua.edu.eg Department of Basic Science, Faculty of Engineering, Pharos University, Alexandria, EgyptSearch for more papers by this authorAzza M. Elbagoury, Azza M. Elbagoury Department of Basic Science, Faculty of Engineering, Pharos University, Alexandria, EgyptSearch for more papers by this authorMoustafa H. Aly, Moustafa H. Aly orcid.org/0000-0003-1966-3755 Department of Electronics and Communications Engineering, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria, EgyptSearch for more papers by this author Wessam M. Salama, Corresponding Author Wessam M. Salama wessam.salama@pua.edu.eg Department of Basic Science, Faculty of Engineering, Pharos University, Alexandria, EgyptSearch for more papers by this authorAzza M. Elbagoury, Azza M. Elbagoury Department of Basic Science, Faculty of Engineering, Pharos University, Alexandria, EgyptSearch for more papers by this authorMoustafa H. Aly, Moustafa H. Aly orcid.org/0000-0003-1966-3755 Department of Electronics and Communications Engineering, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria, EgyptSearch for more papers by this author First published: 05 October 2020 https://doi.org/10.1049/iet-ipr.2020.0122Citations: 2AboutSectionsPDF 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 AbstractBreast cancer is a major cause of transience amongst women. In this paper, two novel techniques, ResNet50 and VGG-16, are utilised and re-trained to recognise two classes rather than 1000 classes with high accuracy and low computational requirements. In addition, transfer learning and data augmentation are performed to solve the problem of lack of tagged data. To get a better accuracy, the support vector machine (SVM) classifier is utilised instead of the last fully connected layer. Our models performance are verified utilising k-fold cross-validation. Our proposed techniques are trained and evaluated on three mammographic datasets: mammographic image analysis society, digital database for screening mammography (DDSM) and the curated breast imaging subset of DDSM. This paper explains end-to-end fully convolutional neural networks without any prepossessing or post-processing. The proposed technique of employing ResNet50 hybridised with SVM achieves the best performance, specifically with the DDSM dataset, producing 97.98% accuracy, 98.46% area under the curve, 97.63% sensitivity, 96.51% precision, 95.97% F1 score and computational time 1.8934 s. 1 Introduction Cancer is not just a normal disease, it affects many organs of human body like lungs, pancreas, blood and breasts. In some way, these types of cancers are alike but the way of spread and growth are different. Recent studies have shown that breast cancer is the most common type of cancer among women [1], accounting for about one-third of newly diagnosed cancers in the USA [2]. Breast cancer mortality rates are also high, representing 17% of cancer-related deaths in general [3]. Accurate early detection and diagnosis of breast cancer is critical in increasing the mortality rate. Mammography is, until today, the most useful tool for general population screening. However, accurate detection and diagnosis of a breast lesion, based on the results of mammography, is difficult and highly dependent on the expertise of the radiologist, resulting in a large number of false positives and additional tests [4]. Contrary to conventional methods, machine learning, which requires a hand-crafted phase of extraction of features, is challenging because it is based on domain knowledge. This eliminates the tedious process of engineering and investigating the discrimination ability of the features while facilitating the reproducibility of the methodologies. Since the emergence of deep learning, various works have been published exploiting deep architectures [5]. The convolutional neural network (CNN) is the most common type of deep learning architecture. Arevalo et al. [6] tested and compared different CNNs for the task of mass with two hand-crafted descriptors. Their experimentation was carried out on the dataset of the Breast Cancer Digital Repository Film Mammography [7]. Huynh et al. [8] used the pre-trained AlexNet [9] for the problem of mass diagnosis without further fine-tuning. A scheme that fine-tunes a pre-trained CNN on a digital database for screening mammography (DDSM) database subset is proposed by Jias et al. [10]. Ting et al. [11] have created and trained their breast mass classification network from scratch. Rampun et al. [12] used a set of a slightly modified pre-trained and fine-tuned version of AlexNet on curated breast imaging subset of DDSM (CBIS-DDSM). There is a great distinction between benign and malignant tumours, round or oval shapes are called benign tumours, while unpredictable outline is considered malignant tumours. In addition, support vector machine (SVM) and artificial neural networks [13] are compared with each other for the classification of healthy, abnormal tissues, benign and malignant tumours [14]. The breast cancer classification techniques are introduced in [15]. Many CNN architectures are available, such as CiFarNet [16], AlexNet, GoogLeNet [17], ResNet, VGG16 and VGG19 [18]. Transfer learning is defined as using a pre-trained model from one problem to solve another problem. Training from scratch on a large neural network requires significant data and computing power. Data augmentation expands the training dataset as new samples are generated by applying a series of random transformations to existing data [19]. It has many advantages, including speeding up the convergence process, preventing over-adjustment and increasing generalisation capabilities [20]. The simplest approach is to apply fine transformations on small datasets (translation, zooming, flipping, mirroring, rotation etc.) [21]. In this paper, the main contribution is that two deep neural networks are used to accomplish high rates of breast cancer classification problems, the ResNet50 and VGG16 models, where SVM is integrated into a fully connected layer in both previous networks. Data augmentation technique is employed on DDSM and mammographic image analysis society (MIAS) datasets to overcome the deficiency of datasets, also transfer leaning is performed to reduce consuming time and computational power. The remainder of the paper is structured as follows. Section 2 details the methodology used in the current study for the classification of mammograms as benign or malignant. The obtained results are displayed and discussed in Sections 3 and 4, respectively. This is followed by the main conclusions in Section 5. 2 Methodology This work proposes novel techniques to classify mammography. This work depends on taking the full power of pre-trained Resnet50 and VGG-16 as starting the training from scratch will lead to over-fitting, consume time and need high computational resources. In addition, to overcome the mammography images deficiency, this work utilises the data augmentation and fine-tuning the transferred parameters of pre-trained models. Eventually, the testing performance will be introduced to assess the proposed technique. 2.1 Transfer learning Transfer learning turns out to be useful when dealing with relatively small datasets, e.g. medical images, which are harder to obtain in large numbers than other datasets. Instead of training a deep neural network from scratch, which would require a significant amount of data, power and time, it is often convenient to use a pre-trained model and just fine-tune its performance to simplify and speed up the process. For transfer learning, we apply the pre-trained VGG-16 and ResNet50 models [22]. 2.2 Deep convolution neural network ResNet50 and VGG16 are considered as the most successful deep CNNs for classification in the computer vision field. Both are pre-trained on the ImageNet dataset (1.3 million natural images) with 1000 classes. These images have a patch of size 224 × 244 pixels and with three-colour channels. To fully exploit these deep models in this work, the greyscale mammography images, which are one-channel, will be concatenated along its third dimension. Furthermore, a new classification layer for two classes: benign and malignant, is utilised as last layer instead of 1000 classes. Then, some parameters must be set to start the fine-tuning process on the mammography dataset. Firstly, the iteration number and the primary learning rate are set to 104 and 10−3, respectively, in the ResNet50 model. The number of the epoch is 40. It is observed that, the momentum should be 0.9 and the weight decay should be 5 × 10−4. Gaussian distribution with a zero mean and variance of 10−2 is used to initialise the new classification final layer since only that layer will be trained from scratch. Secondly, the iteration number and the primary learning should be 105 and 10−4, respectively, to re-train the VGG16 model. The new momentum and the weight decay are adjusted to be 0.7 and 4 × 10−3. A Gaussian distribution with a zero mean and variance of 10−3 is used to initialise the new layer as previously mentioned. These parameters are the best fine-tuned for the diagnosis of medical breast cancer. The other parameters are set to default values. 2.3 SVM classifier The SVM is an algorithm for the machine learning which analyses the classification data. It is a supervised method of learning, which separates data into categories. SVM aims to formulate a computationally efficient way of learning by separating hyperplanes in a high dimensional characteristics space. There are many hyperplanes that could distinguish two sets of data. The best hyperplane to choose is the one with the highest range. The margin is defined as the width by which, before hitting a data point, the boundary could increase. Support vectors are the data points pushed up by the margin. Hence, the optimal hyperplane for SVM is the target to find it; which separates clusters of target vectors on the opposing sides of the plane. 2.4 Evaluating the impact of data augmentation Generally, training on a large number of training samples is an important key for achieving high accuracy rate. Nonetheless, due to the limited volume of patients, the biomedical repositories contain a relatively small number of samples. Accordingly, data augmentation is a method for increasing the size of the input data by generating new data from the original input data. There are many data augmentation strategies; rotation is the one used here in our work. Each original image rotates in 0°, 90°, 180° and 270°. Therefore, each image is augmented to four images. 2.5 Evaluation There are several metrics for assessing a classifier, including the precision, the area under the curve (AUC), the accuracy and the F1 score. 2.5.1 Accuracy Accuracy is the indicator of the classifier making the correct prediction. It gives the ability of performance of the whole classifier. The following equation describes the accuracy [23]: (1)where true positive, false negative, true negative and false positive are described by TP, FN, TN and FP, respectively. 2.5.2 Precision Precision is the ratio of positive observations correctly predicted to total positive observations expected. High precision relates to the low false positive rate (FPR) as [23] (2) 2.5.3 F1 score The weighted average of precision and recall are described by F1 score. It is used as a statistical measure to score the classifier efficiency. Therefore, this score takes both false positives and false negatives into account [23] (3) 2.5.4 Area under the receiver operating characteristic (ROC) curve The AUC curve is used within the framework of medical diagnosis. It offers a method for the evaluation of models based on the ROC curve average of every point. The AUC score should always be between ‘0’ and ‘1’ for a classifier. The model with a higher AUC value provides a better performance for the classifier. 2.5.5 Receiver operating characteristic The ROC curve is a graph of operating points which can be viewed as a plot of the true positive rate (TPR) as a function of the FPR. The TPR is also referred to as sensitivity (recall) and is defined as in [23] (4) 3 Results About 322, 564 and 330 mammograms taken from the MIAS, DDSM and CBIS-DDSM databases are investigated to demonstrate the effectiveness of the proposed techniques [24–26]. The selection criteria rely on the availability of tumour containing mammograms, as MIAS, DDSM and CBIS-DDSM databases contain some mammograms with no tumours. The databases cover various cases of abnormality (speculated, III-defined and circumscribed), density of the breast tissues and age of the patients as shown in Table 1. In our work, the mammogram datasets are chosen to verify the proposed methods using Python. The number of training and testing samples for all the datasets are introduced in Table 2. To improve the accuracy, data augmentation is performed to the samples that are rotated by four angles 0°, 90°, 180° and 270° as shown in Table 2. Table 1 demonstrates the details of the dataset description. The mammograms under investigation contain different cases of the fatty, fatty-glandular and dense breast from the MIAS database, and different cases from each of the four density categories (fatty, glandular, heterogeneously dense and extremely dense) of the DDSM and CBIS-DDSM database. Table 1. Description of mammograms investigated from the MIAS, DDSM and CBIS-DDSM databases DDSM database: 564 images (262 benign and malignant 302) Abnormality in gender Speculated Ill-defined Circumscribed 110 250 204 Age ranges for patients, years 30–40 41–50 51–60 61–70 >70 110 197 102 90 65 Density of tissues Density 1 Density 2 Density 3 Density 4 90 212 180 82 MIAS database: 322 images (benign 146 and malignant 176) Age ranges for patients, years 30–40 41–50 51–60 61–70 >70 70 100 63 54 35 Abnormality in gender Speculated Ill-defined Circumscribed 82 70 170 Density of tissues Glandular Fatty Dense 109 119 94 CBIS-DDSM database: 333 images (148 benign and 185 malignant) Abnormality in gender Speculated Ill-defined Circumscribed 64 175 94 Patients age range, years 30–40 41–50 51–60 61–70 >70 30 108 90 45 60 Density of tissues Density 1 Density 2 Density 3 Density 4 72 107 94 60 Table 2. Number of training and testing samples for all the datasets used, data augmentation techniques and number of generated images Datasets Training Testing Total Number of images after augmentation process DDSM 451 113 564 1804 MIAS 257 65 322 1028 CBIS-DDSM — 330 330 — Eventually, the k-fold cross-validation technique is employed [27]. In k-fold cross-validation, it divides all the samples randomly into groups of samples, called k-fold values, of equal sizes. The prediction function is learned using k−1 folds, and the fold left out is used for test and the average classification results for all k-fold values are shown in Table 3. For example, in the five-fold cross-validation, data is divided into five groups randomly, every time one group is selected as a testing set and the other four groups are used as a training set. The five-fold average classification accuracy, sensitivity, precision and AUC are calculated by averaging the classification results of the five rounds. The same way is employed to calculate the average classification results and the standard deviation for all k-fold values. In order to reduce the deviation, the final classification results are obtained by averaging the results of all k-folds cross-validation (from k = 1 to 5) as shown in Table 3. The average classification results and standard deviation are illustrated in Tables 3–9 for all k-folds cross-validation of the ResNet50 and VGG16 using DDSM, MIAS and CBIS-DDSM databases, respectively. For the DDSM, it is obvious from Table 3 that the highest values are obtained by the ResNet50 hybrid with the SVM with linear kernel function. Our framework achieves an accuracy of 97.98% and AUC of 98.46%. Moreover, the sensitivity, precision and F1 score reach 97.63, 96.51 and 95.97%. A comparison between our proposed framework and other CNN architectures in the literature is described in Table 10. Table 3. Average classification results and standard deviation for different k-fold cross-validation of the proposed techniques using DDSM databases Model k-fold value Accuracy, % AUC, % Sensitivity, % Precision, % F1 score, % ResNet50 1 98.34 99.43 98.67 97.89 95.56 2 97.58 99.27 96.79 97.67 95.56 3 97.69 97.16 97.46 96.88 96.46 4 98.49 98.38 97.34 97.56 96.46 5 97.69 98.14 97.88 97.99 95.56 average results 97.98%±0.23 98.46%±0.26 97.63%±0.24 96.51%±0.19 95.97%±0.28 Table 4. Average k-fold cross-validation classification results using VGG16 with attaching the SVM for DDSM dataset Model Accuracy AUC Sensitivity Precision F1 score VGG16 94.54%±0.33 94.65%±0.32 93.87%±0.29 94.98%±0.31 92.24%±0.35 Table 5. Average k-fold cross-validation classification results using ResNet50 and VGG16 without attaching the SVM for DDSM dataset Model Accuracy AUC Sensitivity Precision F1 score ResNet50 92.74%±0.26 91.98%±0.32 91.34%±0.29 90.58%±0.25 91.45%±0.31 VGG16 88.54%±0.34 87.65%±0.31 86.64%±0.31 84.96%±0.29 85.67%±0.37 Table 6. Average k-fold cross-validation classification results using ResNet50 and VGG16 with attaching the SVM for MIAS dataset Model Accuracy AUC Sensitivity Precision F1 score ResNet50 96.99%±0.28 97.81%±0.33 96.88%±0.31 96.99%±0.27 96.45%±0.35 VGG16 94.76%±0.35 95.45%±0.34 95.11%±0.34 92.58%±0.31 92.87%±0.39 Table 7. Average k-fold cross-validation classification results using ResNet50 and VGG16 without attaching the SVM for MIAS dataset Model Accuracy AUC Sensitivity Precision F1 score ResNet50 93.98%±0.28 92.88%±0.25 91.96%±0.26 91.78%±0.29 92.67%±0.23 VGG16 87.94%±0.31 86.98%±0.29 87.44%±0.28 83.66%±0.31 84.97%±0.24 Table 8. Average k-fold cross-validation classification results using ResNet50 and VGG16 with attaching the SVM for CBIS-DDSM Model Accuracy AUC Sensitivity Precision F1 score ResNet50 96.98%±0.21 96..98%±0.23 97.14%±0.25 96.89%±0.29 96.54%±0.28 VGG16 95.94%±0.23 95.66%±0.28 95.87%±0.31 94.48%±0.32 93.67%±0.29 Table 9. Average k-fold cross-validation classification results using ResNet50 and VGG16 without attaching the SVM for CBIS-DDSM Model Accuracy AUC Sensitivity Precision F1 score ResNet50 94.78%±0.19 92.78%±0.21 93.97%±0.23 92.88%±0.28 93.87%±0.25 VGG16 90.94%±0.22 89.75%±0.25 88.87%±0.28 86.96%±0.29 87.67%±0.31 Table 10. Comparative view of several classification methods based on different CNN architectures and datasets and our proposed models Reference Dataset AUC Accuracy [28] dataset obtained by radiologists 0.87 — [8] DDSM — 66% [29] University of Chicago Medical Centre 0.81 — [30] BCDR-F03 0.88 — [14] CBIS-DDSM 0.94 87.2% VGG16 hybrid with SVM DDSM dataset 94.65% 94.54% ResNet50 hybrid with SVM DDSM dataset 98.46% 97.98% The computational efficiency of the proposed framework is investigated to describe its applicability in real-time systems as shown in Table 11. The revealed measurement time is around 1.8934 and 2.3476 s for ResNet50 and VGG16 models, respectively. Table 11. Computational time of the proposed system Model Time, s ResNet50 1.8934 VGG16 2.3476 When using the ResNet50 without and with attaching SVM, the accuracy is 92.74 and 97.98%, respectively. Moreover, when using VGG16, the accuracy is 88.54% and increases to 94.54% after attaching SVM on DDSM dataset as shown in Figs. 1-3–4. Fig. 1Open in figure viewerPowerPoint ResNet50 without attaching SVM algorithm Fig. 2Open in figure viewerPowerPoint ResNet50 with attaching SVM algorithm Fig. 3Open in figure viewerPowerPoint VGG16 without attaching SVM algorithm Fig. 4Open in figure viewerPowerPoint VGG16 with attaching SVM algorithm Fig. 5 explains samples of images from the testing set. Moreover, Fig. 6 provides an example of visualisation of classifier on four patients. The classifier diagnoses patients A and B correctly. Patients C and D are not correctly diagnosed by the classifier. From figures, it is noted that the performance of our model quickly increases and is stabilised to an accuracy >90%. Fig. 5Open in figure viewerPowerPoint Samples images from testing DDSM dataset (a) Malignant, (b) Benign Fig. 6Open in figure viewerPowerPoint Example of DDSM dataset classification (a) True positive, (b) True negative, (c) False positive, (d) False negative 4 Discussion of the results This paper shows that accurate classification of screening mammograms can be achieved with a deep learning model trained in an end-to-end. Tables 1 and 2 explain datasets details, number and distribution. Tables 3–9 assure that our models achieve the best performance. The proposed models are compared with other recent systems in Table 10. In addition, we applied k-fold cross-validation (k = 1, 2, 3, 4, 5) to judge the performance of the proposed approach. The proposed ResNet50 hybrid with SVM achieves the highest accuracy (97.98%) as compared with other approaches. In addition, the accuracy and AUC of the proposed models are higher than the other systems found in literature. The comparison illustrated in Table 10 reveals the superior performance of the proposed models over other existing systems. Figs. 1-3–4 investigate the performance of the proposed models by evaluating the accuracy between tests and training DDSM dataset, showing that training and validation datasets are suitably representative. These figures are called learning curves, which are widely used diagnostic tools for our models that learn from a training dataset incrementally. The model can be evaluated on the training dataset and on a hold out validation dataset after each update during training and plots of the measured performance that can be created to show learning curves. These curves show that the proposed models enhance the accuracy significantly. Fig. 5 explains samples of malignant and benign images from DDSM dataset. Moreover, Fig. 6 evaluates performance of our models and explains the predicted output TP, TN, FP and FN. 5 Conclusion The effectiveness of the proposed techniques is investigated as real clinical mammograms representing different cases of breast thickness, breast size and age of the patients taken from the MIAS, DDSM and CBIS-DDSM databases. The diagnosis performance is evaluated in terms of the accuracy, sensitivity, precision, AUC, F1 score and computational time using k-fold cross-validation. Re-training scenario is introduced, where the training is initialised by weights of a network that has already been trained using another dataset. Since a huge amount of training data is required, the data augmentation technique is utilised to overcome data deficiency and add diversity to the dataset, which strengthen the generalisation ability of the pre-trained network and further alleviate over-fitting. This work is very helpful and proves that there is no need for human interface with pre- or post-processing or hand-crafted features. The ResNet50 hybrid system with SVM achieves the best performance: 97.98% accuracy, 98.46% AUC, 97.63% sensitivity, 96.51% precision and 95.97% F1 score and computational time 1.8934 s. Moreover, when using VGG16, the accuracy is 88.54% and increase to 94.54%. The VGG16 model achieves 94.65% AUC, 93.87% sensitivity, 94.98% precision and 92.24% F1 score after attaching SVM on DDSM datasets, maintaining as low as possible the computational and memory requirements. The robustness of the proposed techniques is investigated by evaluating and comparing with other recent systems, and the obtained results show the superior performance of the proposed systems. 6 References 1Siegel R.L., Miller K.D., Jemal A.: ‘Cancer statistics, 2015’, CA-Cancer J. 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