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首页> 外文期刊>Artificial Intelligence Review: An International Science and Engineering Journal >Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges
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Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges

机译：深入学习的乳腺癌通过医学成像方式进行分类：最先进的挑战和研究挑战

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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.
机译：乳腺癌是全世界妇女的常见和致命的疾病。因此，乳腺癌的早期和精确诊断起重要作用，以改善这种疾病患者的预后。几项研究通过不同的医学成像方式开发了自动化技术以预测乳腺癌发育。然而，很少有审查研究可以重新承载现有的乳腺癌分类文献。这些研究通过使用传统的机器学习方法通​​过手工工程特征，提供了许多癌症类型的分类，分割或分级，包括乳腺癌的分类，分段或分级。本综述侧重于通过最先进的人造深度神经网络方法使用医学成像多方形乳腺癌分类。预计将在五个方面提高程序决策分析，例如成像模态，数据集及其类别的类型，预处理技术，深神经网络类型，以及用于乳腺癌分类的性能度量。从五个上述方面的角度，有条不紊地选择了来自八个学院储存库的四十九个学习刊物，并仔细审查。此外，本研究提供了五个方面的定量，定性和批判性分析。本综述表明，乳房X线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，以及剩余的使用的独占数据集。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。采用几种类型的浅层和深度神经网络结构使用图像对乳腺癌进行分类。经常使用卷积神经网络来构建有效的乳腺癌分类模型。一些所选研究采用预先训练的网络或开发了新的深神经网络来分类乳腺癌。大多数所选研究使用精度和区域下的曲线测量标准，然后是灵敏度，精度和F测量指标，以评估开发的乳腺癌分类模型的性能。最后，本综述向未来学者提供了10个开放的研究挑战，这些挑战是通过各种成像方式开发乳腺癌分类模型的未来学者。该审查可以作为医学图像分类的初学者的宝贵资源，以及通过不同的医学成像方式专注于深入学习的乳腺癌分类的高级科学家。

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