An automated system for classifying types of cerebral hemorrhage based on image processing techniques

Abstract

The brain is one of the most important vital organs in the human body. It is responsible for most of the body’s basic activities, such as breathing, heartbeat, thinking, remembering, speaking, and others. It also controls the central nervous system. Cerebral hemorrhage is considered one of the most dangerous diseases that a person may be exposed to during his life. Therefore, the correct and rapid diagnosis of the hemorrhage type is an important medical issue. The innovation in this work lies in extracting a huge number of effective features from computed tomography (CT) images of the brain using the Orange3 data mining technique, as the number of features extracted from each CT image reached (1,000). The proposed system then uses the extracted features in the classification process through logistic regression (LR), support vector machine (SVM), k-nearest neighbor algorithm (KNN), and convolutional neural networks (CNN), which classify cerebral hemorrhage into four main types: epidural hemorrhage, subdural hemorrhage, intraventricular hemorrhage, and intraparenchymal hemorrhage. A total of (1,156) CT images were tested to verify the validity of the proposed model, and the results showed that the accuracy reached the required success level with an average of (97.1%).