Detection and categorization of acute intracranial hemorrhage subtypes using a multilayer DenseNet‐ResNet architecture with improved random forest classifier

In this article, the detection and categorization of acute intracranial hemorrhage (ICH) subtypes using a multilayer DenseNet‐ResNet architecture with improved random forest classifier (IRF) is proposed to detect the subtypes of intracerebral hemorrhage with high accuracy and less computational time. Here, the brain CT images are taken from the physionet repository publicly dataset. Then the images are preprocessed to eliminate the unwanted noises. After that, the image features are extracted by using multilayer densely connected convolutional network (DenseNet) combined with residual network (ResNet) architecture with multiple convolutional layers. The subtypes are epidural hemorrhage (EDH), subarachnoid hemorrhage (SAH), intraparenchymal hemorrhage (IPH), subdural hemorrhage (SDH), intraventricular hemorrhage (IVH) are classified by using an IRF classifier with high accuracy. The simulation process is carried out in MATLAB site. The proposed multilayer‐DenseNet‐ResNet‐IRF attains higher accuracy 23.44%, 31.93%, 42.83%, 41.9% is compared with the existing methods, such as deep learning algorithm for automatic detection and classification of acute intracranial hemorrhages in head CT scans (ICH‐DC‐2D‐CNN), fusion‐based deep learning along nature‐inspired algorithm for the diagnosis of intracerebral hemorrhage (ICH‐DC‐FSVM), and detection of intracranial hemorrhage on CT scan images using convolutional neural network (ICH‐DC‐CNN) and double fully convolutional networks (FCNs), respectively.