Intracranial Brain Hemorrhage Diagnosis and Classification: A Hybrid Approach

Intracranial brain hemorrhage is a very common problem with a high mortality rate and often can be life-threatening if necessary steps cannot be taken on time. Patients with hemorrhagic cases need to undergo a CT scan of the brain and for taking further steps, the scans should be examined immediately. For this purpose, we proposed a CAD system using a hybrid machine-learning approach which will help radiologists to diagnose intracranial hemorrhage in a more robust way. We used VGG16 and VGG19 models for feature extraction and then trained random forest (RF) and multilayer perceptron (MLP) models with these features. For our research, we have collected a CT brain image dataset that contains 2,501 images with five hemorrhage classes: intraventricular, intraparenchymal, subarachnoid, epidural, and subdural. After training our models it resulted in an overall accuracy of 97.24% using the VGG16-MLP model and 97.02% accuracy using the VGG19-MLP model for classifying brain hemorrhage from CT scans images. A comparative result of our best approach vs. the previous best approach (from our reviewed papers) for each hemorrhage class is as follows; epidural: VGG19-MLP (0.97) vs. YOLOv4 (0.98), intraparenchymal: VGG16-MLP (0.95) vs. YOLOv4 (0.95), intraventricular: VGG19-MLP (0.90) vs. DB-RF (0.97), subarachnoid: VGG19-MLP (0.94) vs. DB-RF (0.90), and subdural: VGG16-MLP (1.00) vs. YOLOv4 (0.95).