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Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges
Artificial Intelligence Review ( IF 12.0 ) Pub Date : 2019-05-25 , DOI: 10.1007/s10462-019-09716-5 Ghulam Murtaza , Liyana Shuib , Ainuddin Wahid Abdul Wahab , Ghulam Mujtaba , Ghulam Mujtaba , Henry Friday Nweke , Mohammed Ali Al-garadi , Fariha Zulfiqar , Ghulam Raza , Nor Aniza Azmi
Artificial Intelligence Review ( IF 12.0 ) Pub Date : 2019-05-25 , DOI: 10.1007/s10462-019-09716-5 Ghulam Murtaza , Liyana Shuib , Ainuddin Wahid Abdul Wahab , Ghulam Mujtaba , Ghulam Mujtaba , Henry Friday Nweke , Mohammed Ali Al-garadi , Fariha Zulfiqar , Ghulam Raza , Nor Aniza Azmi
Breast cancer is a common and fatal disease among women worldwide. Therefore, the early and precise diagnosis of breast cancer plays a pivotal role to improve the prognosis of patients with this disease. Several studies have developed automated techniques using different medical imaging modalities to predict breast cancer development. However, few review studies are available to recapitulate the existing literature on breast cancer classification. These studies provide an overview of the classification, segmentation, or grading of many cancer types, including breast cancer, by using traditional machine learning approaches through hand-engineered features. This review focuses on breast cancer classification by using medical imaging multimodalities through state-of-the-art artificial deep neural network approaches. It is anticipated to maximize the procedural decision analysis in five aspects, such as types of imaging modalities, datasets and their categories, pre-processing techniques, types of deep neural network, and performance metrics used for breast cancer classification. Forty-nine journal and conference publications from eight academic repositories were methodically selected and carefully reviewed from the perspective of the five aforementioned aspects. In addition, this study provided quantitative, qualitative, and critical analyses of the five aspects. This review showed that mammograms and histopathologic images were mostly used to classify breast cancer. Moreover, about 55% of the selected studies used public datasets, and the remaining used exclusive datasets. Several studies employed augmentation, scaling, and image normalization pre-processing techniques to minimize inconsistencies in breast cancer images. Several types of shallow and deep neural network architecture were employed to classify breast cancer using images. The convolutional neural network was utilized frequently to construct an effective breast cancer classification model. Some of the selected studies employed a pre-trained network or developed new deep neural networks to classify breast cancer. Most of the selected studies used accuracy and area-under-the-curve metrics followed by sensitivity, precision, and F-measure metrics to evaluate the performance of the developed breast cancer classification models. Finally, this review presented 10 open research challenges for future scholars who are interested to develop breast cancer classification models through various imaging modalities. This review could serve as a valuable resource for beginners on medical image classification and for advanced scientists focusing on deep learning-based breast cancer classification through different medical imaging modalities.
中文翻译:
通过医学成像方式进行基于深度学习的乳腺癌分类:最新技术和研究挑战
乳腺癌是全球女性常见且致命的疾病。因此,早期准确诊断乳腺癌对于改善该病患者的预后具有举足轻重的作用。几项研究开发了使用不同医学成像方式预测乳腺癌发展的自动化技术。然而,很少有综述研究可以概括现有的关于乳腺癌分类的文献。这些研究通过手工设计的特征使用传统的机器学习方法,概述了许多癌症类型(包括乳腺癌)的分类、分割或分级。本综述侧重于通过最先进的人工深度神经网络方法使用医学成像多模态对乳腺癌进行分类。预计将在五个方面最大化程序决策分析,例如成像模式的类型、数据集及其类别、预处理技术、深度神经网络的类型以及用于乳腺癌分类的性能指标。从上述五个方面有条不紊地筛选和仔细审查了来自八个学术知识库的 49 篇期刊和会议出版物。此外,本研究还对五个方面进行了定量、定性和批判性分析。该综述表明,乳房 X 光照片和组织病理学图像主要用于对乳腺癌进行分类。此外,大约 55% 的所选研究使用公共数据集,其余使用专有数据集。几项研究采用了增强、缩放、和图像标准化预处理技术,以最大限度地减少乳腺癌图像中的不一致性。几种类型的浅层和深层神经网络架构被用于使用图像对乳腺癌进行分类。卷积神经网络经常被用来构建有效的乳腺癌分类模型。一些选定的研究采用了预先训练的网络或开发了新的深度神经网络来对乳腺癌进行分类。大多数选定的研究使用准确性和曲线下面积指标,然后是灵敏度、精度和 F 测量指标来评估开发的乳腺癌分类模型的性能。最后,这篇综述为有兴趣通过各种成像方式开发乳腺癌分类模型的未来学者提出了 10 项开放研究挑战。
更新日期:2019-05-25
中文翻译:
通过医学成像方式进行基于深度学习的乳腺癌分类:最新技术和研究挑战
乳腺癌是全球女性常见且致命的疾病。因此,早期准确诊断乳腺癌对于改善该病患者的预后具有举足轻重的作用。几项研究开发了使用不同医学成像方式预测乳腺癌发展的自动化技术。然而,很少有综述研究可以概括现有的关于乳腺癌分类的文献。这些研究通过手工设计的特征使用传统的机器学习方法,概述了许多癌症类型(包括乳腺癌)的分类、分割或分级。本综述侧重于通过最先进的人工深度神经网络方法使用医学成像多模态对乳腺癌进行分类。预计将在五个方面最大化程序决策分析,例如成像模式的类型、数据集及其类别、预处理技术、深度神经网络的类型以及用于乳腺癌分类的性能指标。从上述五个方面有条不紊地筛选和仔细审查了来自八个学术知识库的 49 篇期刊和会议出版物。此外,本研究还对五个方面进行了定量、定性和批判性分析。该综述表明,乳房 X 光照片和组织病理学图像主要用于对乳腺癌进行分类。此外,大约 55% 的所选研究使用公共数据集,其余使用专有数据集。几项研究采用了增强、缩放、和图像标准化预处理技术,以最大限度地减少乳腺癌图像中的不一致性。几种类型的浅层和深层神经网络架构被用于使用图像对乳腺癌进行分类。卷积神经网络经常被用来构建有效的乳腺癌分类模型。一些选定的研究采用了预先训练的网络或开发了新的深度神经网络来对乳腺癌进行分类。大多数选定的研究使用准确性和曲线下面积指标,然后是灵敏度、精度和 F 测量指标来评估开发的乳腺癌分类模型的性能。最后,这篇综述为有兴趣通过各种成像方式开发乳腺癌分类模型的未来学者提出了 10 项开放研究挑战。