A Simplified Framework for the Detection of Intracranial Hemorrhage in CT Brain Images Using Deep Learning (original) (raw)

Current Medical Imaging Formerly Current Medical Imaging Reviews

Background: The need for accurate and timely detection of Intracranial hemorrhage (ICH) is of utmost importance to avoid untoward incidents that may even lead to death. Hence, this presented work leverages the ability of a pretrained deep convolutional neural network (CNN) for the detection of ICH in computed tomography (CT) brain images. Methods: Different frameworks have been analyzed for their effectiveness for the classification of CT brain images into hemorrhage or non-hemorrhage conditions. All these frameworks were investigated on the CQ500 dataset. Furthermore, an exclusive preprocessing pipeline was designed for both normal and ICH CT images. Firstly, a framework involving the pretrained deep CNN, AlexNet, has been exploited for both feature extraction and classification using the transfer learning method. Secondly, a modified AlexNet-Support vector machine (SVM) classifier is explored, and finally, a feature selection method, Principal Component Analysis (PCA), has been in...

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