Intracranial Hemorrhage Detection Using Neural Network Based Methods With Federated Learning
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2020
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| _version_ | 1866910587111342080 |
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| author | Srivastava, Utkarsh Chandra Singh, Anshuman Kumar, K. Sree |
| author_facet | Srivastava, Utkarsh Chandra Singh, Anshuman Kumar, K. Sree |
| contents | Intracranial hemorrhage, bleeding that occurs inside the cranium, is a serious health problem requiring rapid and often intensive medical treatment. Such a condition is traditionally diagnosed by highly-trained specialists analyzing computed tomography (CT) scan of the patient and identifying the location and type of hemorrhage if one exists. We propose a neural network approach to find and classify the condition based upon the CT scan. The model architecture implements a time distributed convolutional network. We observed accuracy above 92% from such an architecture, provided enough data. We propose further extensions to our approach involving the deployment of federated learning. This would be helpful in pooling learned parameters without violating the inherent privacy of the data involved. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2005_08644 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Intracranial Hemorrhage Detection Using Neural Network Based Methods With Federated Learning Srivastava, Utkarsh Chandra Singh, Anshuman Kumar, K. Sree Computer Vision and Pattern Recognition Machine Learning Image and Video Processing Intracranial hemorrhage, bleeding that occurs inside the cranium, is a serious health problem requiring rapid and often intensive medical treatment. Such a condition is traditionally diagnosed by highly-trained specialists analyzing computed tomography (CT) scan of the patient and identifying the location and type of hemorrhage if one exists. We propose a neural network approach to find and classify the condition based upon the CT scan. The model architecture implements a time distributed convolutional network. We observed accuracy above 92% from such an architecture, provided enough data. We propose further extensions to our approach involving the deployment of federated learning. This would be helpful in pooling learned parameters without violating the inherent privacy of the data involved. |
| title | Intracranial Hemorrhage Detection Using Neural Network Based Methods With Federated Learning |
| topic | Computer Vision and Pattern Recognition Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2005.08644 |