Brain Hemorrhage Detection based on Heat Maps, Autoencoder and CNN Architecture

Abstract

Brain hemorrhage refers to hemorrhage within the brain tissue or between the surrounding bone. Therefore, head hemorrhage can lead to many dangerous consequences, especially brain hemorrhage. Early and correct intervention by experts in such cases is important for the patient's life. In this study, computed tomography images of brain hemorrhage are classified by AlexNet which is one of the convolutional neural network models used recently in the biomedical field. In this scope, the data set is restructured with the autoencoder network model and heat maps of each image in the data set are extracted to improve the classification success. The number of images in the data set is then increased by approximately 10 times using the data augmentation technique. The classification process is performed using support vector machines. As a result, the best success rate in the classification was 98.57%. In conclusion, the proposed approach contributed to the classification of cerebral hemorrhage images.

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