Deep Learning Applications in Medical Image Analysis

Abstract

The tremendous success of machine learning algorithms at image recognition tasks in recent years intersects with a time of dramatically increased use of electronic medical records and diagnostic imaging. This review introduces the machine learning algorithms as applied to medical image analysis, focusing on convolutional neural networks, and emphasizing clinical aspects of the field. The advantage of machine learning in an era of medical big data is that significant hierarchal relationships within the data can be discovered algorithmically without laborious hand-crafting of features. We cover key research areas and applications of medical image classification, localization, detection, segmentation, and registration. We conclude by discussing research obstacles, emerging trends, and possible future directions.

Keywords

Computer scienceConvolutional neural networkArtificial intelligenceDeep learningMedical imagingField (mathematics)Image segmentationSegmentationBig dataMachine learningFeature extractionKey (lock)Image (mathematics)Cover (algebra)Contextual image classificationData scienceData mining

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Publication Info

Year: 2017
Type: article
Volume: 6
Pages: 9375-9389
Citations: 1409
Access: Closed

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APA Style

                            
                                    Justin Ker, 
                                
                                    Lipo Wang, 
                                
                                    Jai Prashanth Rao
                                
                                et al.
                            
                            (2017). 
                            Deep Learning Applications in Medical Image Analysis. 
                            IEEE Access
                            , 6
                            
                            , 9375-9389.
                            https://doi.org/10.1109/access.2017.2788044

Identifiers

DOI: 10.1109/access.2017.2788044