Feasible Study on Intracranial Hemorrhage Detection and Classification using a CNN-LSTM Network

Abstract

Intracranial hemorrhage (ICH) is a life-threatening condition, the outcome of which is associated with stroke, trauma, aneurysm, vascular malformations, high blood pressure, illicit drugs and blood clotting disorders. In this study, we presented the feasibility of the automatic identification and classification of ICH using a head CT image based on deep learning technique. The subtypes of ICH for the classification was intraparenchymal, intraventricular, subarachnoid, subdural and epidural. We first performed windowing to provide three different images: brain window, bone window and subdural window, and trained 4,516,842 head CT images using CNN-LSTM model. We used the Xception model for the deep CNN, and 64 nodes and 32 timesteps for LSTM. For the performance evaluation, we tested 727,392 head CT images, and found the resultant weighted multi-label logarithmic loss was 0.07528. We believe that our proposed method enhances the accuracy of ICH identification and classification and can assist radiologists in the interpretation of head CT images, particularly for brain-related quantitative analysis.