Deep Learning Models for Intracranial Hemorrhage Recognition: A comparative study

Abstract

Every day, a large number of people with brain injury are received in the emergency rooms. Due to the large number of slices analyzed by the doctors for each patient and to accelerate the diagnosis, the development of a precise computer-aided diagnosis system becomes very recommended.

The aim of our work is developing a tool to help radiologists in the detection of intracranial hemorrhage (ICH) and its five (05) subtypes in computed tomography (CT) images. Five deep learning models are tested: ResNet50, VGG16, Xception, InceptionV3 and InceptionResNetV2. Before training these models, preprocessing operations are performed like normalization and windowing. The experiments show that VGG-16 architecture provides the best performances. The model achieves an accuracy of 96%.