A review on deep learning in medical image analysis

被引:211
|
作者
Suganyadevi, S. [1 ]
Seethalakshmi, V [1 ]
Balasamy, K. [2 ]
机构
[1] KPR Inst Engn & Technol, Dept ECE, Coimbatore, Tamil Nadu, India
[2] Dr Mahalingam Coll Engn & Technol, Dept IT, Coimbatore, Tamil Nadu, India
关键词
Deep learning; Survey; Image classes; Medical image analysis; Accuracy; CONVOLUTIONAL NEURAL-NETWORK; CLASSIFICATION; SEGMENTATION; REPRESENTATION; CORONAVIRUS; MASS;
D O I
10.1007/s13735-021-00218-1
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Ongoing improvements in AI, particularly concerning deep learning techniques, are assisting to identify, classify, and quantify patterns in clinical images. Deep learning is the quickest developing field in artificial intelligence and is effectively utilized lately in numerous areas, including medication. A brief outline is given on studies carried out on the region of application: neuro, brain, retinal, pneumonic, computerized pathology, bosom, heart, breast, bone, stomach, and musculoskeletal. For information exploration, knowledge deployment, and knowledge-based prediction, deep learning networks can be successfully applied to big data. In the field of medical image processing methods and analysis, fundamental information and state-of-the-art approaches with deep learning are presented in this paper. The primary goals of this paper are to present research on medical image processing as well as to define and implement the key guidelines that are identified and addressed.
引用
收藏
页码:19 / 38
页数:20
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