Survey of deep learning in breast cancer image analysis

Debelee, Taye Girma; Schwenker, Friedhelm; Ibenthal, Achim; Yohannes, Dereje

doi:10.1007/s12530-019-09297-2

Cited by 134 publications

(57 citation statements)

References 138 publications

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“…The survey of [31] summarizes the deep learning applications in breast cancer image analysis in Screen-File Mammography (SFM), Digital Mammography (DM), US, MRI, and Digital fast tomosynthesis (DBT) imaging modes, respectively. At the same time, six papers are found based on the topic of our interest.…”

Section: B Motivation Of Our Review Papermentioning

confidence: 99%

A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

et al. 2020

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Breast cancer is one of the most common and deadliest cancers among women. Since histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of breast cancers. To improve the accuracy and objectivity of Breast Histopathological Image Analysis (BHIA), Artificial Neural Network (ANN) approaches are widely used in the segmentation and classification tasks of breast histopathological images. In this review, we present a comprehensive overview of the BHIA techniques based on ANNs. First of all, we categorize the BHIA systems into classical and deep neural networks for in-depth investigation. Then, the relevant studies based on BHIA systems are presented. After that, we analyze the existing models to discover the most suitable algorithms. Finally, publicly accessible datasets, along with their download links, are provided for the convenience of future researchers.

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Section: B Motivation Of Our Review Papermentioning

confidence: 99%

A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

et al. 2020

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“…ResNet-50 outperformed ResNet-34 in terms of accuracy. In [ 25 ], Debelee et al presented a detailed review of DL techniques used in breast cancer image analysis. They reviewed recent publicly available databases of breast cancer images and discussed the modalities that are used for breast cancer imaging.…”

Section: Literature Surveymentioning

confidence: 99%

Computer Aided Breast Cancer Detection Using Ensembling of Texture and Statistical Image Features

Roy

Das

Kar

et al. 2021

Sensors

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Breast cancer, like most forms of cancer, is a fatal disease that claims more than half a million lives every year. In 2020, breast cancer overtook lung cancer as the most commonly diagnosed form of cancer. Though extremely deadly, the survival rate and longevity increase substantially with early detection and diagnosis. The treatment protocol also varies with the stage of breast cancer. Diagnosis is typically done using histopathological slides from which it is possible to determine whether the tissue is in the Ductal Carcinoma In Situ (DCIS) stage, in which the cancerous cells have not spread into the encompassing breast tissue, or in the Invasive Ductal Carcinoma (IDC) stage, wherein the cells have penetrated into the neighboring tissues. IDC detection is extremely time-consuming and challenging for physicians. Hence, this can be modeled as an image classification task where pattern recognition and machine learning can be used to aid doctors and medical practitioners in making such crucial decisions. In the present paper, we use an IDC Breast Cancer dataset that contains 277,524 images (with 78,786 IDC positive images and 198,738 IDC negative images) to classify the images into IDC(+) and IDC(-). To that end, we use feature extractors, including textural features, such as SIFT, SURF and ORB, and statistical features, such as Haralick texture features. These features are then combined to yield a dataset of 782 features. These features are ensembled by stacking using various Machine Learning classifiers, such as Random Forest, Extra Trees, XGBoost, AdaBoost, CatBoost and Multi Layer Perceptron followed by feature selection using Pearson Correlation Coefficient to yield a dataset with four features that are then used for classification. From our experimental results, we found that CatBoost yielded the highest accuracy (92.55%), which is at par with other state-of-the-art results—most of which employ Deep Learning architectures. The source code is available in the GitHub repository.

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“…However, Lee et al [ 7 ] in their survey paper indicated that the existence of deep learning in the research community has become a reason to use object recognition in an image. In addition to object detection, deep learning has been applied for feature extraction, abnormality detection, cancer/tumor segmentation and classification [ 11 ].…”

Section: Deep Learning In Tumor Detection Segmentation and Classificationmentioning

confidence: 99%

“…Breast cancer occurs when there is uncontrolled growth of cells in the breast [ 12 ]. It is the most widely diagnosed type of cancer in women and the first prevalent cancer type in Ethiopia [ 11 , 13 ]. There are four types of breast cancer manifestation that include: mass, calcification, architectural distortion and bilateral asymmetry [ 11 ].…”

Section: Deep Learning In Tumor Detection Segmentation and Classificationmentioning

confidence: 99%

Deep Learning in Selected Cancers’ Image Analysis—A Survey

et al. 2020

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Deep learning algorithms have become the first choice as an approach to medical image analysis, face recognition, and emotion recognition. In this survey, several deep-learning-based approaches applied to breast cancer, cervical cancer, brain tumor, colon and lung cancers are studied and reviewed. Deep learning has been applied in almost all of the imaging modalities used for cervical and breast cancers and MRIs for the brain tumor. The result of the review process indicated that deep learning methods have achieved state-of-the-art in tumor detection, segmentation, feature extraction and classification. As presented in this paper, the deep learning approaches were used in three different modes that include training from scratch, transfer learning through freezing some layers of the deep learning network and modifying the architecture to reduce the number of parameters existing in the network. Moreover, the application of deep learning to imaging devices for the detection of various cancer cases has been studied by researchers affiliated to academic and medical institutes in economically developed countries; while, the study has not had much attention in Africa despite the dramatic soar of cancer risks in the continent.

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Survey of deep learning in breast cancer image analysis

Cited by 134 publications

References 138 publications

A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

Computer Aided Breast Cancer Detection Using Ensembling of Texture and Statistical Image Features

Deep Learning in Selected Cancers’ Image Analysis—A Survey

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