A review on deep learning in medical image analysis.
Suganyadevi SV SeethalakshmiK BalasamyPublished in: International journal of multimedia information retrieval (2021)
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.
Keyphrases
- deep learning
- artificial intelligence
- big data
- healthcare
- convolutional neural network
- machine learning
- public health
- health information
- heart failure
- atrial fibrillation
- emergency department
- resting state
- diabetic retinopathy
- subarachnoid hemorrhage
- functional connectivity
- blood brain barrier
- global health
- electronic health record
- bone regeneration