Affiliations: [a] Department of Biomedical Engineering, Near East University, Near East Boulevard, TRNC, Nicosia, Turkey
| [b] The Gordon Center for Medical Imaging, Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
Correspondence:
[*]
Corresponding author. Abdulkader Helwan, Department of Biomedical Engineering, Near East University, Near East Boulevard, TRNC, Nicosia, 99138 Turkey. E-mail: WEML [email protected].
Abstract: Deep learning algorithms have recently been applied to solving challenging problems in medicine such as medical image classification and analysis. In some areas, those algorithms have outperformed the human medical experts experience in diagnosis. Thus, in this paper we apply three different deep networks to solve the problem of brain hemorrhage identification in CT images. The motivation behind this work is the difficulty that radiologists encounter when diagnosing a hemorrhagic brain CT image, in particularly in the early stages of the brain bleeding. Autoencoder (AE), stacked autoencoder (SAE), and convolutional neural network (CNN) are employed and trained to classify the CT images into hemorrhagic or non-hemorrhagic. Experimentally, it was found that all employed networks performed differently in terms of accuracy, error reached, and training time. However, stacked autoencoder has achieved a higher accuracy and lesser error compared to other used networks.
Keywords: Deep learning, deep networks, hemorrhage, autoencoder, stacked autoencoder, convolutional neural network
