PERFORMANCE EVALUATION OF DIFFERENT DEEP LEARNING MODELS FOR CLASSIFYING ISCHEMIC, HEMORRHAGIC, AND NORMAL COMPUTED TOMOGRAPHY IMAGES: TRANSFER LEARNING APPROACHES

Abstract

A stroke is a case of damage to a brain area due to a sudden decrease or complete cessation of blood flow to the brain. The interruption or reduction of the transportation of oxygen and nutrients through the bloodstream causes damage to brain tissues. Thus, motor or sensory impairments occur in the body part controlled by the affected area of the brain. There are primarily two main types of strokes: ischemic and hemorrhagic. When a patient is suspected of having a stroke, a computed tomography scan is performed to identify any tissue damage and facilitate prompt intervention quickly. Early intervention can prevent the patient from being permanently disabled throughout their lifetime. This study classified ischemic, hemorrhage, and normal computed tomography images taken from international databases as open source with AlexNet, ResNet50, GoogleNet, InceptionV3, ShuffleNet, and SqueezeNet deep learning models using transfer learning approach. The data were divided into 80% training and 20% testing, and evaluation metrics were calculated by five-fold cross-validation. The best performance results for the three-class output were obtained with AlexNet as 0.9086±0.02 precision, 0.9097±0.02 sensitivity, 0.9091±0.02 F1 score, 0.9089±0.02 accuracy. The average area under curve values was obtained with AlexNet 0.9920±0.005 for ischemia, 0.9828±0.008 for hemorrhage, and 0.9686±0.012 for normal.