Abstract

Deep learning algorithms have recently been applied for image detection and classification, lately with good results in the medicine such as medical image analysis. This paper aims to support the detection of intracranial hemorrhage in computed tomography (CT) images using deep learning algorithms and convolutional neural networks (CNN). The motivation of this work is the difficulty of physicians when they face the task to identify intracranial hemorrhage, especially when they are in the primary stages of brain bleeding, making a misdiagnosis. A total of 491 CT studies were used to train and evaluate two convolutional neuronal networks in the task of classifying hemorrhage or non-hemorrhage. The proposed CNN networks reach 97% of recall, 98% accuracy and 98% of F1 measure.