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Dr. Shuihua Wang
Department of Mathematics, University of Leicester, Leicester (LE1 7RH), United Kingdom

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0 Data Analysis
0 Deep Learning
0 Pattern Recognition
0 Transfer Learning
0 biomedical image analysis

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Journal article
Published: 13 June 2020 in Neural Computing and Applications
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Computer-aided diagnosis system is becoming a more and more important tool in clinical treatment, which can provide a verification of the doctors’ decisions. In this paper, we proposed a novel abnormal brain detection method for magnetic resonance image. Firstly, a pre-trained AlexNet was modified with batch normalization layers and trained on our brain images. Then, the last several layers were replaced with an extreme learning machine. A searching method was proposed to find the best number of layers to be replaced. Finally, the extreme learning machine was optimized by chaotic bat algorithm to obtain better classification performance. Experiment results based on 5 × hold-out validation revealed that our method achieved state-of-the-art performance.

ACS Style

Siyuan Lu; Shui-Hua Wang; Yu-Dong Zhang. Detection of abnormal brain in MRI via improved AlexNet and ELM optimized by chaotic bat algorithm. Neural Computing and Applications 2020, 1 -13.

AMA Style

Siyuan Lu, Shui-Hua Wang, Yu-Dong Zhang. Detection of abnormal brain in MRI via improved AlexNet and ELM optimized by chaotic bat algorithm. Neural Computing and Applications. 2020; ():1-13.

Chicago/Turabian Style

Siyuan Lu; Shui-Hua Wang; Yu-Dong Zhang. 2020. "Detection of abnormal brain in MRI via improved AlexNet and ELM optimized by chaotic bat algorithm." Neural Computing and Applications , no. : 1-13.