We propose a novel method that combines a convolutional neural network (CNN) with a
long short-term memory (LSTM) mechanism for accurate prediction of intracranial …

In this paper, we present our system for the RSNA Intracranial Hemorrhage Detection
challenge, which is based on the RSNA 2019 Brain CT Hemorrhage dataset. The proposed …

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 …

We describe a deep learning approach for automated brain hemorrhage detection from
computed tomography (CT) scans. Our model emulates the procedure followed by …

Background: The need for accurate and timely detection of Intracranial hemorrhage (ICH) is
of utmost importance to avoid untoward incidents that may even lead to death. Hence, this …

Computed tomography (CT) of the head is used worldwide to diagnose neurologic
emergencies. However, expertise is required to interpret these scans, and even highly …

Acute Intracranial hemorrhage (ICH) is a life-threatening disease that requires emergency
medical attention, which is routinely diagnosed using non-contrast head CT imaging. The …

Initial results are reported on automated detection of intracranial hemorrhage from CT, which
would be valuable in a computer-aided diagnosis system to help the radiologist detect …

Background Deep learning (DL) algorithms are playing an increasing role in automatic
medical image analysis. Purpose To evaluate the performance of a DL model for the …

To investigate the performance of a joint convolutional neural networks-recurrent neural
networks (CNN-RNN) using an attention mechanism in identifying and classifying …