In the field of stroke imaging, deep learning (DL) has enormousuntapped potential.When clinically significant symptoms of a cerebral strokeare detected, it is crucial to make an urgent diagnosis using available imagin...In the field of stroke imaging, deep learning (DL) has enormousuntapped potential.When clinically significant symptoms of a cerebral strokeare detected, it is crucial to make an urgent diagnosis using available imagingtechniques such as computed tomography (CT) scans. The purpose of thiswork is to classify brain CT images as normal, surviving ischemia or cerebralhemorrhage based on the convolutional neural network (CNN) model. In thisstudy, we propose a computer-aided diagnostic system (CAD) for categorizingcerebral strokes using computed tomography images. Horizontal flip datamagnification techniques were used to obtain more accurate categorization.Image Data Generator to magnify the image in real time and apply anyrandom transformations to each training image. An early stopping method toavoid overtraining. As a result, the proposed methods improved several estimationparameters such as accuracy and recall, compared to other machinelearning methods. A python web application was created to demonstrate theresults of CNN model classification using cloud development techniques. Inour case, the model correctly identified the drawing class as normal with 79%accuracy. Based on the collected results, it was determined that the presentedautomated diagnostic system could be used to assist medical professionals indetecting and classifying brain strokes.展开更多
文摘In the field of stroke imaging, deep learning (DL) has enormousuntapped potential.When clinically significant symptoms of a cerebral strokeare detected, it is crucial to make an urgent diagnosis using available imagingtechniques such as computed tomography (CT) scans. The purpose of thiswork is to classify brain CT images as normal, surviving ischemia or cerebralhemorrhage based on the convolutional neural network (CNN) model. In thisstudy, we propose a computer-aided diagnostic system (CAD) for categorizingcerebral strokes using computed tomography images. Horizontal flip datamagnification techniques were used to obtain more accurate categorization.Image Data Generator to magnify the image in real time and apply anyrandom transformations to each training image. An early stopping method toavoid overtraining. As a result, the proposed methods improved several estimationparameters such as accuracy and recall, compared to other machinelearning methods. A python web application was created to demonstrate theresults of CNN model classification using cloud development techniques. Inour case, the model correctly identified the drawing class as normal with 79%accuracy. Based on the collected results, it was determined that the presentedautomated diagnostic system could be used to assist medical professionals indetecting and classifying brain strokes.
