Breast Cancer Image Classification Research Articles

Deep Learning on Histopathology Images for Breast Cancer Classification: A Bibliometric Analysis.

Medical imaging is gaining significant attention in healthcare, including breast cancer. Breast cancer is the most common cancer-related death among women worldwide. Currently, histopathology image analysis is the clinical gold standard in cancer diagnosis. However, the manual process of microscopic examination involves laborious work and can be misleading due to human error. Therefore, this study explored the research status and development trends of deep learning on breast cancer image classification using bibliometric analysis. Relevant works of literature were obtained from the Scopus database between 2014 and 2021. The VOSviewer and Bibliometrix tools were used for analysis through various visualization forms. This study is concerned with the annual publication trends, co-authorship networks among countries, authors, and scientific journals. The co-occurrence network of the authors’ keywords was analyzed for potential future directions of the field. Authors started to contribute to publications in 2016, and the research domain has maintained its growth rate since. The United States and China have strong research collaboration strengths. Only a few studies use bibliometric analysis in this research area. This study provides a recent review on this fast-growing field to highlight status and trends using scientific visualization. It is hoped that the findings will assist researchers in identifying and exploring the potential emerging areas in the related field.

Histopathological image-based breast cancer detection employing 3D-convolutional neural network feature extraction and Stochastic Diffusion Kernel Recursive Neural Networks classification

ABSTRACT Among the various cancer categories, breast cancer diagnoses are commonly found in many women, and automatic classification of breast cancer images is a crucial task using computer-aided analysis. This paper proposed a method for the detection of histopathological image-based breast cancer and the main motive of the technique is to employ feature extraction and classification based on DL techniques. The input image is filtered using edge smoothening for noise removal and image resizing on the pre-processing method and used 3D-Convolutional Neural network (3D_CNN) for extracting the features of the image process. Extracted deep features have been given as input for the classification process using Stochastic Diffusion Kernel Recursive Neural Networks (SDKRNN). The suggested model combines the sturdiness of convolutional and kernel blocks and demonstrates performance stability when compared to existing techniques. The experimental results are assessed using a variety of performance metrics and compared to current breast cancer detection methodologies. The result demonstrated the experimental evaluation analyzed for various datasets in terms of accuracy, precision, F-1 score, specificity, and AUC of 98%, 93.8%, 89%, 80%, and 70% and it determined the outperforms in the detection of breast cancer.

A Study on Multi-class Classification of Breast Cancer Images using Ensemble Network and Transfer Learning

Background: Breast cancer causes millions of deaths all over the world every year. It has become the most common type of cancer in women. Early detection will help in better prognosis and increase the chance of survival. Automating the classification using Computer-Aided Diagnosis (CAD) systems can make the diagnosis less prone to errors. Multi-class classification and Binary classification of breast cancer is a challenging problem. Convolutional neural network architectures extract specific feature descriptors from images, which cannot represent different types of breast cancer. This leads to false positives in classification, which is undesirable in disease diagnosis. Methods: The current paper presents an ensemble Convolutional neural network for multi-class classification and Binary classification of breast cancer. The feature descriptors from each network are combined to produce the final classification. In this paper, histopathological images are taken from the publicly available BreakHis dataset and classified into 8 classes. Results: The proposed ensemble model can perform better when compared to the methods proposed in the literature. The results showed that the proposed model could be a viable approach for breast cancer classification. Conclusion: In this paper, an approach for multi-class classification on the breast images for cancer detection is proposed. The proposed architecture can be a viable option for the classification of histopathology images.

Deep hybrid architectures for binary classification of medical breast cancer images

The diagnosis of breast cancer in the early stages significantly decreases the mortality rate by allowing the choice of adequate treatment. This study developed and evaluated twenty-eight hybrid architectures combining seven recent deep learning techniques for feature extraction (DenseNet 201, Inception V3, Inception ReseNet V2, MobileNet V2, ResNet 50, VGG16, and VGG19), and four classifiers (MLP, SVM, DT, and KNN) for a binary classification of breast pathological images over the BreakHis and FNAC datasets. The designed architectures were evaluated using: (1) four classification performance criteria (accuracy, precision, recall, and F1-score), (2) Scott Knott (SK) statistical test to cluster the proposed architectures and identify the best cluster of the outperforming architectures, and (3) the Borda Count voting method to rank the best performing architectures. The results showed the potential of combining deep learning techniques for feature extraction and classical classifiers to classify breast cancer in malignant and benign tumors. The hybrid architecture using the MLP classifier and DenseNet 201 for feature extraction (MDEN) was the top performing architecture with higher accuracy values reaching 99% over the FNAC dataset, 92.61%, 92%, 93.93%, and 91.73% over the four magnification factor values of the BreakHis dataset: 40X, 100X, 200X, and 400X, respectively. The results of this study recommend the use of hybrid architectures using DenseNet 201 for the feature extraction of the breast cancer histological images because it gave the best results for both datasets BreakHis and FNAC, especially when combined with the MLP classifier.

An investigation of XGBoost-based algorithm for breast cancer classification

Breast cancer is one of the leading cancers affecting women around the world. The Computer-Aided Diagnosis (CAD) system is a powerful tool to assist pathologists during the process of diagnosing cancer, which effectively identifies the presence of cancerous cells. A standard CAD system includes processes of pre-processing, feature extraction, feature selection and classification. In this paper, we propose an enhanced breast cancer classification technique called Deep Learning and eXtreme Gradient Boosting (DLXGB) on histopathology breast cancer images using the BreaKHis dataset. This method first applies data augmentation and stain normalization for pre-processing, then pre-trained DenseNet201 will automatically learn features within an image and combine with a powerful gradient boosting classifier. The proposed classification technique is designed to classify breast cancer histology images into binary benign and malignant, and additionally one of eight non-overlapping/overlapping categories: i.e., Adenosis (A), Fibroadenoma (F), Phyllodes Tumour (PT), And Tubular Adenoma (TA) Ductal Carcinoma (DC), Lobular Carcinoma (LC), Mucinous Carcinoma (MC), And Papillary Carcinoma (PC). With DLXGB, we have obtained an accuracy of 97% for both binary and multi-classification improving the exiting work done by researchers using the BreaKHis dataset. The results indicated that this method could produce a powerful prediction for breast cancer image classification.

An SVM approach towards breast cancer classification from H&E-stained histopathology images based on integrated features.

Breast cancer is one among the most frequent reasons of women's death worldwide. Nowadays, healthcare informatics is mainly focussing on the classification of breast cancer images, due to the lethal nature of this cancer. There are chances of inter- and intra-observer variability that may lead to misdiagnosis in the detection of cancer. This study proposed an automatic breast cancer classification system that uses support vector machine (SVM) classifier based on integrated features (texture, geometrical, and color). The University of California Santa Barbara (UCSB) dataset and BreakHis dataset, which are available in public domain, were used. A classification comparison module which involves SVM, k-nearest neighbor (k-NN), random forest (RF), and artificial neural network (ANN) was also proposed to determine the classifier that best suits for the application of breast cancer detection from histopathology images. The performance of these classifiers was analyzed against metrics like accuracy, specificity, sensitivity, balanced accuracy, and F-score. Results showed that among the classifiers, the SVM classifier performed better with a test accuracy of approximately 90% on both the datasets. Additionally, the significance of the proposed integrated SVM model was statistically analyzed against other classifier models.

Deep learning for breast cancer classification: Enhanced tangent function

AbstractRecently, deep learning using convolutional neural network (CNN) has been used successfully to classify the images of breast cells accurately. However, the accuracy of manual classification of those histopathological images is comparatively low. This research aims to increase the accuracy of the classification of breast cancer images by utilizing a patch‐based classifier (PBC) along with deep learning architecture. The proposed system consists of a deep convolutional neural network that helps in enhancing and increasing the accuracy of the classification process. This is done by the use of the PBC. CNN has completely different layers where images are first fed through convolutional layers using hyperbolic tangent function together with the max‐pooling layer, drop out layers, and SoftMax function for classification. Further, the output obtained is fed to a PBC that consists of patch‐wise classification output followed by majority voting. The results are obtained throughout the classification stage for breast cancer images that are collected from breast‐histology datasets. The proposed solution improves the accuracy of classification whether or not the images had normal, benign, in‐situ, or invasive carcinoma from 87% to 94% with a decrease in processing time from 0.45 to 0.2 s on average. The proposed solution focused on increasing the accuracy of classifying cancer in the breast by enhancing the image contrast and reducing the vanishing gradient. Finally, this solution for the implementation of the contrast limited adaptive histogram equalization technique and modified tangent function helps in increasing the accuracy.

Breast Cancer Image Classification: A Review.

Breast cancer represents uncontrolled breast cell growth. Breast cancer is the most diagnosed cancer in women worldwide. Early detection of breast cancer improves the chances of survival and increases treatment options. There are various methods for screening breast cancer, such as mammogram, ultrasound, computed tomography and Magnetic Resonance Imaging (MRI). MRI is gaining prominence as an alternative screening tool for early detection and breast cancer diagnosis. Nevertheless, MRI can hardly be examined without the use of a Computer-Aided Diagnosis (CAD) framework, due to the vast amount of data. This paper aims to cover the approaches used in the CAD system for the detection of breast cancer. In this paper, the methods used in CAD systems are categories into two classes: the conventional approach and artificial intelligence (AI) approach. The conventional approach covers the basic steps of image processing, such as preprocessing, segmentation, feature extraction and classification. The AI approach covers the various convolutional and deep learning networks used for diagnosis. This review discusses some of the core concepts used in breast cancer and presents a comprehensive review of efforts in the past to address this problem.

An Effective Mammogram Classification Using Hot Based Tree And Hot Based Cnn

Breast cancer is the subsequent leading cause of cancer-related deceases among women. Initial exposure stimulates enhanced visualization and saves survives. These days, the exact grouping classification of breast cancer images is a difficult errand. There are much research works delivering various strategies and algorithms for this specific errand of medical image processing. To build up an exact characterization, this paper presents a viable classification of mammogram images utilizing HOT based classification tree and HOT based convolutional neural network (CNN). The input breast image is at first taken from the database and pre-processed by RGB to grayscale conversion and normalization methodology. In this way Histogram of Oriented Texture (HOT) Descriptor is extorted from the pre-processed images. At long last images are classified as typical or irregular utilizing HOT based classification tree and HOT based CNN. The exploratory results show that the introduced method outperforms the existing strategies concerning various performance assessments like accuracy, sensitivity, specificity, mean absolute error, AUC score, kappa statistics, and Root mean square error

Deep learning in mammography images segmentation and classification: Automated CNN approach

In this work, a new framework for breast cancer image segmentation and classification is proposed. Different models including InceptionV3, DenseNet121, ResNet50, VGG16 and MobileNetV2 models, are applied to classify Mammographic Image Analysis Society (MIAS), Digital Database for Screening Mammography (DDSM) and the Curated Breast Imaging Subset of DDSM (CBIS-DDSM) into benign and malignant. Moreover, the trained modified U-Net model is utilized to segment breast area from the mammogram images. This method will aid as a radiologist's assistant in early detection and improve the efficiency of our system. The Cranio Caudal (CC) vision and Mediolateral Oblique (MLO) view are widely used for the identification and diagnosis of breast cancer. The accuracy of breast cancer diagnosis will be improved as the number of views is increased. Our proposed frame work is based on MLO view and CC view to enhance the system performance. In addition, the lack of tagged data is a big challenge. Transfer learning and data augmentation are applied to overcome this problem. Three mammographic datasets; MIAS, DDSM and CBIS-DDSM, are utilized in our evaluation. End-to-end fully convolutional neural networks (CNNs) are introduced in this paper. The proposed technique of applying data augmentation with modified U-Net model and InceptionV3 achieves the best result, specifically with the DDSM dataset. This achieves 98.87% accuracy, 98.88% area under the curve (AUC), 98.98% sensitivity, 98.79% precision, 97.99% F1 score, and a computational time of 1.2134 s on DDSM datasets.

Conventional Machine Learning versus Deep Learning for Magnification Dependent Histopathological Breast Cancer Image Classification: A Comparative Study with Visual Explanation.

Breast cancer is a serious threat to women. Many machine learning-based computer-aided diagnosis (CAD) methods have been proposed for the early diagnosis of breast cancer based on histopathological images. Even though many such classification methods achieved high accuracy, many of them lack the explanation of the classification process. In this paper, we compare the performance of conventional machine learning (CML) against deep learning (DL)-based methods. We also provide a visual interpretation for the task of classifying breast cancer in histopathological images. For CML-based methods, we extract a set of handcrafted features using three feature extractors and fuse them to get image representation that would act as an input to train five classical classifiers. For DL-based methods, we adopt the transfer learning approach to the well-known VGG-19 deep learning architecture, where its pre-trained version on the large scale ImageNet, is block-wise fine-tuned on histopathological images. The evaluation of the proposed methods is carried out on the publicly available BreaKHis dataset for the magnification dependent classification of benign and malignant breast cancer and their eight sub-classes, and a further validation on KIMIA Path960, a magnification-free histopathological dataset with 20 image classes, is also performed. After providing the classification results of CML and DL methods, and to better explain the difference in the classification performance, we visualize the learned features. For the DL-based method, we intuitively visualize the areas of interest of the best fine-tuned deep neural networks using attention maps to explain the decision-making process and improve the clinical interpretability of the proposed models. The visual explanation can inherently improve the pathologist’s trust in automated DL methods as a credible and trustworthy support tool for breast cancer diagnosis. The achieved results show that DL methods outperform CML approaches where we reached an accuracy between 94.05% and 98.13% for the binary classification and between 76.77% and 88.95% for the eight-class classification, while for DL approaches, the accuracies range from 85.65% to 89.32% for the binary classification and from 63.55% to 69.69% for the eight-class classification.

Retraction: Detection and Classification of Breast Cancer Using improved Grey Wolf Algorithm (IOP Conf. Ser.: Mater. Sci. Eng. 1084 012023)

This article has been retracted by IOP Publishing following an allegation that this article may contain tortured phrases [1]. IOP Publishing has investigated in line with the COPE guidelines and have found tortured phrases to be masking overlap of other work [2-3] and agrees this work should be retracted. IOP Publishing wishes to credit PubPeer commenters [4] and the Problematic Paper Screener for bringing this issue to our attention. The authors have neither confirmed whether they agree or disagree to this retraction. [1] Cabanac G, Labbe C, Magazinov A, 2021, Tortured phrases: A dubious writing style emerging in science. Evidence of critical issues affecting established journals. arXiv:2107.06751v1 [2] Zou L, Yu S, Meng T, Zhang Z, Liang X, Xie Y, 2019, A Technical Review of Convolutional Neural Network-Based Mammographic Breast Cancer Diagnosis", Computational and Mathematical Methods in Medicine 2019 [3] Nahid A, Kong Y, 2017, Involvement of Machine Learning for Breast Cancer Image Classification: A Survey, Computational and Mathematical Methods in Medicine 2017 [4] https://pubpeer.com/publications/D2890893EA445EE73047F71B625304

Classification of Breast Cancer Images Using Ensembles of Transfer Learning

It is a challenging task to estimate the cancerous cells and tissues via computer-aided diagnosis systems on high-resolution histopathological images. In this study, it is suggested to use transfer learning and ensemble learning methods together in order to reduce the difficulty of this task and better diagnose cancer patients. In the studies, histopathological images with 40× and 100× magnification factors are analyzed. In order to prove the success of the study with experimental studies, firstly, the results provided by pre-modeled deep learning architectures trained by histopathological image dataset, then the results acquired by different transfer learning approaches and the results obtained with the ensembles of deeply learned features using transfer learning methods are presented comparatively. Three different approaches are applied for transfer learning by fine-tuning the pre-trained convolution neural networks. In the experimental section, results of single classifiers (i.e., support vector machines, logistic regression, k-nearest neighbor and bagging) are presented by employing features of CNN models obtained by defined transfer learning approaches. Then, decisions of each classifier model are combined separately by weighted decision fusion (WDF) and stacking decision fusion (SDF) ensemble learning methods that have proven to improve the classification performance of the proposed classification system.

Optimized radial basis neural network for classification of breast cancer images

Breast cancer is a curable disease if the diagnosed at an early stage. The chances of having breast cancer are the lowest in the married woman after the breast-feeding phase because the cancer is formed from the blocked milk ducts. Nowadays, cancer is considered the leading cause of death globally. Breast cancer is the most common cancer among females. It is possible to develop breast cancer while breast-feeding a baby, but it is rare. Mammography is one of the most effective methods used in hospitals and clinics for early detection of breast cancer. Various researchers are used in artificial intelligence-based mammogram techniques. This process of mammography will reduce the death rate of the patients affected by breast cancer. This process is improved by image analysing, detection, screening, diagnosing, and other performance measures. The radial basis neural network will be used for classification purposes. The radial basis neural network is designed with the help of the optimization algorithm. The optimization is to tune the classifier to reduce the error rate with the minimum time for the training process. The cuckoo search algorithm will be used for this purpose. Thus, the proposed optimum RBNN is determined to classify breast cancer images. In this, the three sets of properties were classified by performing the feature extraction and feature reduction. In this breast cancer MRI image, the normal, benign, and malignant is taken to perform the classification. The minimum fitness value is determined to evaluate the optimum value of possible locations. The radial basis function is evaluated with the cuckoo search algorithm to optimize the feature reduction process. The proposed methodology is compared with the traditional radial basis neural network using the evaluation parameter like accuracy, precision, recall and f1-score. The whole system model is done by using Matrix Laboratory (MATLAB) with the adaptation of 2018a since the proposed system is most efficient than most recent related literature. Thus, it concluded with the efficient classification process of RBNN using a cuckoo search algorithm for breast cancer images. The mammogram images are taken into recent research because breast cancer is a major issue for women. This process is carried to classify the various features for three sets of properties. The optimized classifier improves performance and provides a better result. In this proposed research work, the input image is filtered using a wiener filter and the classifier extracts the feature based on the breast image.

An ensemble algorithm for breast cancer histopathology image classification

Breast cancer is considered among prime reasons of deaths among women. Early detection can improve the survival rate and can decrease the mortality rate too. Though it’s a difficult and tedious task for pathologists. The motive of this research is to assess the efficiency and accuracy of various deep learning models and ensemble methods. Authors have proposed Stacked Generalized Ensemble algorithm that classifies the images into benign and malignant. The dataset used in experimentation is H&E breast cancer image dataset. Experimental results show that SGE has outperformed on various deep learning single classifiers. The evaluation criteria used for measuring the efficiency of algorithm is accuracy, precision, recall and F1 measure. A comparative analysis has been done with the existing deep learning methods. All the experimental results demonstrate that the Stacked Generalized Ensemble approach performed exponentially good on the histopathological breast cancer image classification and achieved an accuracy of 97.53%.

Histopathological Classification of Breast Cancer Images Using a Multi-Scale Input and Multi-Feature Network.

Diagnosis of pathologies using histopathological images can be time-consuming when many images with different magnification levels need to be analyzed. State-of-the-art computer vision and machine learning methods can help automate the diagnostic pathology workflow and thus reduce the analysis time. Automated systems can also be more efficient and accurate, and can increase the objectivity of diagnosis by reducing operator variability. We propose a multi-scale input and multi-feature network (MSI-MFNet) model, which can learn the overall structures and texture features of different scale tissues by fusing multi-resolution hierarchical feature maps from the network’s dense connectivity structure. The MSI-MFNet predicts the probability of a disease on the patch and image levels. We evaluated the performance of our proposed model on two public benchmark datasets. Furthermore, through ablation studies of the model, we found that multi-scale input and multi-feature maps play an important role in improving the performance of the model. Our proposed model outperformed the existing state-of-the-art models by demonstrating better accuracy, sensitivity, and specificity.

Convolutional Neural Network Based Breast Cancer Histopathology Image Classification.

Breast cancer is a global health concern, with approximately 30 million new cases projected to be reported by 2030. While efforts are being channeled into curative measures, preventive and diagnostic measures also need to be improved to curb the situation. Convolutional Neural Networks (CNNs) are a class of deep learning algorithms that have been widely adopted for the computerized classification of breast cancer histopathology images. In this work, we propose a set of training techniques to improve the performance of CNN-based classifiers for breast cancer identification. We combined transfer learning techniques with data augmentation and whole image training to improve the performance of the CNN classifier. Instead of conventional image patch extraction for training and testing, we employed a high-resolution whole-image training and testing on a modified network that was pre-trained on the Imagenet dataset. Despite the computational complexity, our proposed classifier achieved significant improvement over the previously reported studies on the open-source BreakHis dataset, with an average image level accuracy of about 91% and patient scores as high as 95%.Clinical Relevance- this work improves on the performance of CNN for breast cancer histopathology image classification. An improved Breast cancer image classification can be used for the preliminary examination of tissue slides in breast cancer diagnosis.

Optimized Radial Basis Neural Network for Classification of Breast Cancer Images.

Breast cancer is a curable disease if diagnosed at an early stage. The chances of having breast cancer are the lowest in married women after the breast-feeding phase because the cancer is formed from the blocked milk ducts. Nowadays, cancer is considered the leading cause of death globally. Breast cancer is the most common cancer among females. It is possible to develop breast cancer while breast-feeding a baby, but it is rare. Mammography is one of the most effective methods used in hospitals and clinics for early detection of breast cancer. Various researchers are used in artificial intelligence- based mammogram techniques. This process of mammography will reduce the death rate of the patients affected by breast cancer. This process is improved by the image analysing, detection, screening, diagnosing, and other performance measures. The radial basis neural network will be used for classification purposes. The radial basis neural network is designed with the help of the optimization algorithm. The optimization is to tune the classifier to reduce the error rate with the minimum time for the training process. The cuckoo search algorithm will be used for this purpose. Thus, the proposed optimum RBNN is determined to classify breast cancer images. In this, the three sets of properties were classified by performing the feature extraction and feature reduction. In this breast cancer MRI image, the normal, benign, and malignant is taken to perform the classification. The minimum fitness value is determined to evaluate the optimum value of possible locations. The radial basis function is evaluated with the cuckoo search algorithm to optimize the feature reduction process. The proposed methodology is compared with the traditional radial basis neural network using the evaluation parameter like accuracy, precision, recall and f1-score. The whole system model is done by using Matrix Laboratory (MATLAB) with the adaptation of 2018a since the proposed system is most efficient than most recent related literature. Thus, it concluded with the efficient classification process of RBNN using a cuckoo search algorithm for breast cancer images. The mammogram images are taken into recent research because breast cancer is a major issue for women. This process is carried to classify the various features for three sets of properties. The optimized classifier improves performance and provides a better result. In this proposed research work, the input image is filtered using a wiener filter, and the classifier extracts the feature based on the breast image.

Analysis of Histopathological Images for Prediction of Breast Cancer Using Traditional Classifiers with Pre-Trained CNN

Breast cancer is one of the most commonly found and dangerous cancer among women which leads to a major research topic in medical science. Most of the times, it is identified by using a biopsy method where tissue is removed and studied under a microscope. If a histopathologist is not well trained then this may lead to the wrong diagnosis. In order to facilitate better diagnosis, automatic analysis of histopathology images can help pathologists to identify malignant tumors and cancer subtypes. Recently, Convolutional Neural Networks (CNNs) have become preferred deep learning approaches for breast cancer classification and detection. In this research, two machine learning approaches i.e. Support Vector Machine (SVM) and Logistic Regression (LR) are used for comparative analysis. This paper mainly focuses on leveraging pre-trained (CNN) activation features on traditional classifiers to perform automatic classification of breast cancer images. For this purpose, a two-phase model has been proposed for automatic classification on the basis of magnification subsequently classify the samples for benign and malignant. This model is trained separately with respect to various image magnifications (40x, 100x, 200x and 400x). In this study, the dataset is partitioned into the following fashion: 80% for the training phase and 20% for testing phase The performance is analyzed by using Accuracy, Precision, Recall and F1-score in order to find out the best-suited model that can be used for automation system. The experimental results demonstrate that ResNet50 network has achieved maximum accuracy for LR in comparison to SVM in magnification factor. In addition, results show that the performance of CNN +LR is slightly better than CNN +SVM for classification of benign and malignant classes. The proposed model helps in extracting more accurate image features and significantly improves the overall accuracy of classification.

Breast Cancer Image Classification via Multi-Network Features and Dual-Network Orthogonal Low-Rank Learning

Histopathological image analysis is an important technique for early diagnosis and detection of breast cancer in clinical practice. However, it has limited efficiency and thus the detection of breast cancer is still an open issue in medical image analysis. To improve the early diagnostic accuracy of breast cancer and reduce the workload of doctors, we devise a classification framework based on histology images by combining deep learning with machine learning methodologies in this paper. Specifically, we devise a multi-network feature extraction model by using pre-trained deep convolution neural networks (DCNNs), develop an effective feature dimension reduction method and train an ensemble support vector machine (E-SVM). First, we preprocess the histological images via scale transformation and color enhancement methods. Second, the multi-network features are extracted by using four pre-trained DCNNs (e.g., DenseNet-121, ResNet-50, multi-level InceptionV3, and multi-level VGG-16). Third, a feature selection method via dual-network orthogonal low-rank learning (DOLL) is further developed for performance boosting and overfitting alleviation. Finally, an E-SVM is trained via fused features and voting strategy to perform the classification task, which classifies the images into four classes (i.e., benign, in situ carcinomas, invasive carcinomas, and normal). We evaluate the proposed method on the public ICIAR 2018 Challenge dataset of histology images of breast cancer and achieve a high classification accuracy of 97.70%. Experimental results show that our method can achieve quite promising performance and outperform state-of-the-art methods.