Automated classification of dolphin whistles based on the convolutional neural network

2019 ◽  
Vol 146 (4) ◽  
pp. 2984-2984
Author(s):  
Gang Qiao ◽  
Lei Li ◽  
Songzuo Liu ◽  
Xin Qing
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Automated Classification

Abstract WP73: Automatic Classification of Clinical MRI Stroke Datasets With a Recurrent Convolutional Neural Network

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Yichuan Liu ◽  
Brandon L Hancock ◽  
Tri Hoang ◽  
Mark R Etherton ◽  
Steven J Mocking ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Human Error ◽  
Clinical Information ◽  
Stroke Care ◽  
Automated Classification ◽  
Aggregate Information ◽  
Clinical Mri ◽  
Testing Set

Background: Fundamental advances in stroke care will require pooling imaging phenotype data from multiple centers, to complement the current aggregation of genomic, environmental, and clinical information. Sharing clinically acquired MRI data from multiple hospitals is challenging due to inherent heterogeneity of clinical data, where the same MRI series may be labeled differently depending on vendor and hospital. Furthermore, the de-identification process may remove data describing the MRI series, requiring human review. However, manually annotating the MRI series is not only laborious and slow but prone to human error. In this work, we present a recurrent convolutional neural network (RCNN) for automated classification of the MRI series. Methods: We randomly selected 1000 subjects from the MRI-GENetics Interface Exploration study and partitioned them into 800 training, 100 validation and 100 testing subjects. We categorized the MRI series into 24 groups (see Table). The RCNN used a modified AlexNet to extract features from 2D slices. AlexNet was pretrained on ImageNet photographs. Since clinical MRI are 3D and 4D, a gated recurrent unit neural network was used to aggregate information from multiple 2D slices to make the final prediction. Results: We achieved a classification accuracy (correct/total cases) of 99.8%, 98.5% and 97.5% on the training, validation and testing set, respectively. The averaged F1-score (percent overlap between predicted cases and actual cases) over all categories were 99.8% 98.2% and 94.4% on the training, validation and testing set. Conclusion: We showed that automated annotation of MRI series by repurposing deep-learning techniques used for photographic image recognition tasks is feasible. Such methods can be used to facilitate high throughput curation of MRI data acquired across multiple centers and enable scientifically productive collaboration by researchers and, ultimately enhancing big data stroke research.

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