Diagnosis of periventricular leukomalacia in children with artificial intelligence-based models developed using brain magnetic resonance images

Abstract

Periventricular leukomalacia is periventricular white matter damage that develops due to hypoxia and ischemia of the brain. It is one of the leading causes of neurological and developmental problems in children that will affect their future lives. Therefore, the correct diagnosis is important for giving the appropriate treatment. The main imaging method used in the diagnosis is magnetic resonance imaging (MRI). In this study, we evaluated the detectability of periventricular leukomalacia with artificial intelligence models in MRIs in children. In the study, two new artificial intelligence-based models are proposed to classify brain MRIs. The first proposed model consists of 19 layers, and this new model was more successful than previously trained deep models for classifying MRIs. In addition, the number of layers is lower than the models accepted in the literature. In the second model we proposed, the features were taken from our first model and optimized with the neighborhood component analysis (NCA) method, and then classified in the wide neural network. The accuracy values obtained in the models we have proposed are 94.62 and 98.92%, respectively. These accuracy values show that our proposed model is successful in classifying MRIs.