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

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

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Computer-aided Diagnosis of Breast Cancer in Ultrasonography Images by Deep Learning

Neurocomputing ◽

10.1016/j.neucom.2021.11.047 ◽

2021 ◽

Author(s):

Xiaofeng Qi ◽

Fasheng Yi ◽

Lei Zhang ◽

Yao Chen ◽

Yong Pi ◽

...

Keyword(s):

Breast Cancer ◽

Deep Learning ◽

Computer Aided Diagnosis ◽

Computer Aided ◽

Aided Diagnosis

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Computer Aided Diagnosis System for Breast Cancer using ID3 and SVM Based on Slantlet Transform

Qalaai Zanist Scientific Journal ◽

10.25212/lfu.qzj.2.2.16 ◽

2017 ◽

Vol 2 (2) ◽

Author(s):

Mohammed Salih Mahdi

Keyword(s):

Breast Cancer ◽

Computer Aided Diagnosis ◽

Diagnosis System ◽

Computer Aided ◽

Aided Diagnosis

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A pilot study of probe-based confocal laser endomicroscopy for computer-aided diagnosis of bile duct cancer by using the deep learning technology

10.26226/morressier.59a6b34bd462b80290b55741 ◽

2017 ◽

Author(s):

Koichi Furukawa

Keyword(s):

Deep Learning ◽

Pilot Study ◽

Bile Duct ◽

Bile Duct Cancer ◽

Confocal Laser Endomicroscopy ◽

Confocal Laser ◽

Learning Technology ◽

Computer Aided ◽

Duct Cancer ◽

Aided Diagnosis

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Breast Cancer Detection and Classification using Traditional Computer Vision Techniques: A Comprehensive Review

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405616666200406110547 ◽

2020 ◽

Vol 16 ◽

Cited By ~ 3

Author(s):

Saliha Zahoor ◽

Ikram Ullah Lali ◽

Muhammad Attique Khan ◽

Kashif Javed ◽

Waqar Mehmood

Keyword(s):

Breast Cancer ◽

Computer Aided Diagnosis ◽

Diagnose Breast Cancer ◽

Initial Stage ◽

Computer Aided ◽

Diagnosis And Classification ◽

And Performance ◽

Tools And Techniques ◽

Aided Diagnosis

: Breast Cancer is a common dangerous disease for women. In the world, many women died due to Breast cancer. However, in the initial stage, the diagnosis of breast cancer can save women's life. To diagnose cancer in the breast tissues there are several techniques and methods. The image processing, machine learning and deep learning methods and techniques are presented in this paper to diagnose the breast cancer. This work will be helpful to adopt better choices and reliable methods to diagnose breast cancer in an initial stage to survive the women's life. To detect the breast masses, microcalcifications, malignant cells the different techniques are used in the Computer-Aided Diagnosis (CAD) systems phases like preprocessing, segmentation, feature extraction, and classification. We have been reported a detailed analysis of different techniques or methods with their usage and performance measurement. From the reported results, it is concluded that for the survival of women’s life it is essential to improve the methods or techniques to diagnose breast cancer at an initial stage by improving the results of the Computer-Aided Diagnosis systems. Furthermore, segmentation and classification phases are challenging for researchers for the diagnosis of breast cancer accurately. Therefore, more advanced tools and techniques are still essential for the accurate diagnosis and classification of breast cancer.

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553 A computer-aided diagnosis system for bullous disease based on deep learning

Journal of Investigative Dermatology ◽

10.1016/j.jid.2019.03.629 ◽

2019 ◽

Vol 139 (5) ◽

pp. S95

Author(s):

Y. Wang ◽

X. He ◽

F. Li ◽

W. Zhu

Keyword(s):

Deep Learning ◽

Computer Aided Diagnosis ◽

Diagnosis System ◽

Bullous Disease ◽

Computer Aided ◽

Aided Diagnosis

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Quantifying the uncertainty of deep learning-based computer-aided diagnosis for patient safety

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2019-0057 ◽

2019 ◽

Vol 5 (1) ◽

pp. 223-226

Author(s):

Max-Heinrich Laves ◽

Sontje Ihler ◽

Tobias Ortmaier ◽

Lüder A. Kahrs

Keyword(s):

Monte Carlo ◽

Patient Safety ◽

Deep Learning ◽

Epistemic Uncertainty ◽

Computer Aided Diagnosis ◽

Uncertainty Estimation ◽

Test Time ◽

Prediction Uncertainty ◽

Computer Aided ◽

Aided Diagnosis

AbstractIn this work, we discuss epistemic uncertainty estimation obtained by Bayesian inference in diagnostic classifiers and show that the prediction uncertainty highly correlates with goodness of prediction. We train the ResNet-18 image classifier on a dataset of 84,484 optical coherence tomography scans showing four different retinal conditions. Dropout is added before every building block of ResNet, creating an approximation to a Bayesian classifier. Monte Carlo sampling is applied with dropout at test time for uncertainty estimation. In Monte Carlo experiments, multiple forward passes are performed to get a distribution of the class labels. The variance and the entropy of the distribution is used as metrics for uncertainty. Our results show strong correlation with ρ = 0.99 between prediction uncertainty and prediction error. Mean uncertainty of incorrectly diagnosed cases was significantly higher than mean uncertainty of correctly diagnosed cases. Modeling of the prediction uncertainty in computer-aided diagnosis with deep learning yields more reliable results and is therefore expected to increase patient safety. This will help to transfer such systems into clinical routine and to increase the acceptance of machine learning in diagnosis from the standpoint of physicians and patients.

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A Review of Computer-Aided Expert Systems for Breast Cancer Diagnosis

Cancers ◽

10.3390/cancers13112764 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2764

Author(s):

Xin Yu Liew ◽

Nazia Hameed ◽

Jeremie Clos

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Deep Learning ◽

Main Process ◽

Learning Approaches ◽

Learning Methods ◽

Advantages And Disadvantages ◽

Computer Aided ◽

Conventional Methods ◽

The Impact

A computer-aided diagnosis (CAD) expert system is a powerful tool to efficiently assist a pathologist in achieving an early diagnosis of breast cancer. This process identifies the presence of cancer in breast tissue samples and the distinct type of cancer stages. In a standard CAD system, the main process involves image pre-processing, segmentation, feature extraction, feature selection, classification, and performance evaluation. In this review paper, we reviewed the existing state-of-the-art machine learning approaches applied at each stage involving conventional methods and deep learning methods, the comparisons within methods, and we provide technical details with advantages and disadvantages. The aims are to investigate the impact of CAD systems using histopathology images, investigate deep learning methods that outperform conventional methods, and provide a summary for future researchers to analyse and improve the existing techniques used. Lastly, we will discuss the research gaps of existing machine learning approaches for implementation and propose future direction guidelines for upcoming researchers.

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The Role and Impact of Deep Learning Methods in Computer-Aided Diagnosis Using Gastrointestinal Endoscopy

Diagnostics ◽

10.3390/diagnostics11040694 ◽

2021 ◽

Vol 11 (4) ◽

pp. 694

Author(s):

Xuejiao Pang ◽

Zijian Zhao ◽

Ying Weng

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Clinical Practice ◽

Gastrointestinal Endoscopy ◽

Computer Aided Diagnosis ◽

Learning Methods ◽

Learning Abilities ◽

Cad System ◽

Computer Aided ◽

Aided Diagnosis

At present, the application of artificial intelligence (AI) based on deep learning in the medical field has become more extensive and suitable for clinical practice compared with traditional machine learning. The application of traditional machine learning approaches to clinical practice is very challenging because medical data are usually uncharacteristic. However, deep learning methods with self-learning abilities can effectively make use of excellent computing abilities to learn intricate and abstract features. Thus, they are promising for the classification and detection of lesions through gastrointestinal endoscopy using a computer-aided diagnosis (CAD) system based on deep learning. This study aimed to address the research development of a CAD system based on deep learning in order to assist doctors in classifying and detecting lesions in the stomach, intestines, and esophagus. It also summarized the limitations of the current methods and finally presented a prospect for future research.

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