Deep Learning in Selected Cancers' Image Analysis-A Survey.
Deep Learning in Selected Cancers' Image Analysis-A Survey.
复制标题
深度学习在选定癌症图像分析中的应用。
DOI：
10.3390/jimaging6110121
复制
发表时间：
2020-11-10
影响因子：
3.2
通讯作者：
Shewarega ZM
中科院分区：
文献类型：
--
作者：
Debelee TG;Kebede SR;Schwenker F;Shewarega ZM
深度学习算法已成为医学图像分析、人脸识别和情感识别的首选方法。在这项调查中，研究和回顾了几种应用于乳腺癌、宫颈癌、脑肿瘤、结肠癌和肺癌的基于深度学习的方法。深度学习已应用于几乎所有用于宫颈癌和乳腺癌的成像方式以及脑肿瘤的 MRI。审查过程的结果表明深度学习方法在肿瘤检测、分割、特征提取和分类方面已经达到了最先进的水平。正如本文所述，深度学习方法以三种不同的模式使用，包括从头开始训练、通过冻结深度学习网络的某些层进行迁移学习以及修改架构以减少网络中存在的参数数量。此外，经济发达国家的学术和医疗机构的研究人员已经研究了深度学习在成像设备中的应用，以检测各种癌症病例；然而，尽管非洲大陆的癌症风险急剧上升，但这项研究在非洲并没有引起太多关注。
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.