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A Modified Deep Convolutional Neural Network for Abnormal Brain Image Classification
Deep learning techniques have gained significant importance among artificial intelligence techniques for any computing applications. Among them, deep convolutional neural networks (DCNNs) is one of the widely used deep learning networks for any practical applications. The accuracy is generally high...
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Published in: | IEEE access 2019, Vol.7, p.4275-4283 |
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Main Authors: | , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Deep learning techniques have gained significant importance among artificial intelligence techniques for any computing applications. Among them, deep convolutional neural networks (DCNNs) is one of the widely used deep learning networks for any practical applications. The accuracy is generally high and the manual feature extraction process is not necessary in these networks. However, the high accuracy is achieved at the cost of huge computational complexity. The complexity in DCNN is mainly due to: 1) increased number of layers between input and output layers and 2) two set of parameters (one set of filter coefficients and another set of weights) in the fully connected network need to be adjusted. In this paper, the second aspect is targeted to reduce the computational complexity of conventional DCNN. Suitable modifications are performed in the training algorithm to reduce the number of parameter adjustments. The weight adjustment process in the fully connected layer is completely eliminated in the proposed modified approach. Instead, a simple assignment process is used to find the weights of this fully connected layer. Thus, the computational complexity is significantly reduced in the proposed approach. The application of modified DCNN is explored in the context of magnetic resonance brain tumor image classification. Abnormal brain tumor images from four different classes are used in this paper. The experimental results show promising results for the proposed approach. |
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ISSN: | 2169-3536 2169-3536 |
DOI: | 10.1109/ACCESS.2018.2885639 |