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. 2019 Jun 3:2019:4629859.
doi: 10.1155/2019/4629859. eCollection 2019.

Application of Deep Learning in Neuroradiology: Brain Haemorrhage Classification Using Transfer Learning

Awwal Muhammad Dawud  1 , Kamil Yurtkan  1 , Huseyin Oztoprak  1
Affiliations

Application of Deep Learning in Neuroradiology: Brain Haemorrhage Classification Using Transfer Learning

Awwal Muhammad Dawud et al. Comput Intell Neurosci. .

Erratum in

Abstract

In this paper, we address the problem of identifying brain haemorrhage which is considered as a tedious task for radiologists, especially in the early stages of the haemorrhage. The problem is solved using a deep learning approach where a convolutional neural network (CNN), the well-known AlexNet neural network, and also a modified novel version of AlexNet with support vector machine (AlexNet-SVM) classifier are trained to classify the brain computer tomography (CT) images into haemorrhage or nonhaemorrhage images. The aim of employing the deep learning model is to address the primary question in medical image analysis and classification: can a sufficient fine-tuning of a pretrained model (transfer learning) eliminate the need of building a CNN from scratch? Moreover, this study also aims to investigate the advantages of using SVM as a classifier instead of a three-layer neural network. We apply the same classification task to three deep networks; one is created from scratch, another is a pretrained model that was fine-tuned to the brain CT haemorrhage classification task, and our modified novel AlexNet model which uses the SVM classifier. The three networks were trained using the same number of brain CT images available. The experiments show that the transfer of knowledge from natural images to medical images classification is possible. In addition, our results proved that the proposed modified pretrained model "AlexNet-SVM" can outperform a convolutional neural network created from scratch and the original AlexNet in identifying the brain haemorrhage.

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Figures

Figure 1
Figure 1
Convolutional neural network.
Figure 2
Figure 2
Sample of the databases training and validating images. (a) Haemorrhage images; (b) normal images.
Figure 3
Figure 3
Proposed CNN architecture.
Figure 4
Figure 4
A sample of the brain images collected from the Internet to test the robustness of the system [41].
Figure 5
Figure 5
AlexNet proposed transfer learning network for the haemorrhage classification.
Figure 6
Figure 6
Modified AlexNet (AlexNet-SVM).
Figure 7
Figure 7
Learning curve for the trained CNN.
Figure 8
Figure 8
Learning curves of AlexNet.
Figure 9
Figure 9
Learning curves of AlexNet-SVM.
Figure 10
Figure 10
Learned kernels of CNN.
Figure 11
Figure 11
Learned kernels of AlexNet.
See this image and copyright information in PMC

References

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