Intracranial Hemorrhage Detection in CT Scans using Deep Learning
2020 IEEE Sixth International Conference on Big Data Computing Service and Applications (BigDataService)
In intracranial hemorrhage treatment patient mortality depends on prompt diagnosis based on a radiologist's assessment of CT scans. In this paper, we investigate the intracranial hemorrhage detection problem and built a deep learning model to accelerate the time used to identify the hemorrhages. To assist with this process, a deep learning model can be used to accelerate the time it takes to identify them. In particular, we built a convolutional neural network based on ResNet for the classification. Using 752,803 DICOM files collected from four international universities by the Radiological Society of North America (RSNA) , we trained and tested a ResNet- 50 based model for predicting the hemorrhage type. Our model has an accuracy of 93.3% in making the correct multiclass prediction and an average per-class recall score of 76%. We show it is possible to achieve an average recall of 86% while maintaining 70% precision via tuning the prediction thresholds. Lastly, we show real-world applicability by deploying a simple web application. The source code for training, metrics evaluation and web application is available at .
deep learning, feature recognition, head computed tomography, Intracranial hemorrhage
Tomasz Lewick, Meera Kumar, Raymond Hong, and Wencen Wu. "Intracranial Hemorrhage Detection in CT Scans using Deep Learning" 2020 IEEE Sixth International Conference on Big Data Computing Service and Applications (BigDataService) (2020): 169-172. https://doi.org/10.1109/BigDataService49289.2020.00033