SMOTE-based convolutional neural network for Alzheimer's disease image classification - Details

author：

Jia, Yuhan (Jia, Yuhan.) ^[1] | Zheng, Minghan (Zheng, Minghan.) ^[2] | Lin, Pengyu (Lin, Pengyu.) ^[3] | Toe, Teoh Teik (Toe, Teoh Teik.) ^[4]

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Abstract：

This　study　explores　the　potential　of　a　convolutional　neural　network　(CNN)　combined　with　the　SMOTE　technique　for　classifying　magnetic　resonance　images　(MRI)　related　to　Alzheimer＇s　disease.　By　incorporating　SMOTE,　we　aim　to　address　challenges　associated　with　data　imbalance.　The　research　also　presents　a　comparison　between　this　approach　and　traditional　methods,　offering　insights　into　its　relative　performance.　©　2024　IEEE.

Keyword：

Convolution Convolutional neural networks Image classification Magnetic resonance Magnetic resonance imaging Neurodegenerative diseases

Community：

[ 1 ] [Jia, Yuhan]Southwest Jiaotong University, Chengdu, China
[ 2 ] [Zheng, Minghan]Harbin Institute of Technology, Weihai, China
[ 3 ] [Lin, Pengyu]Fuzhou University, Fuzhou, China
[ 4 ] [Toe, Teoh Teik]Ntu Business Ai Lab, Nanyang Technological University, Singapore

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SMOTE-based convolutional neural network for Alzheimer's disease image classification
2024，2024 4th International Conference on Neural Networks, Information and Communication Engineering, NNICE 2024

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Source ：

Year： 2024

Page： 330-334

Language： English

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ESI Highly Cited Papers on the List： 0 Unfold All

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Chinese Cited Count：

30 Days PV： 2

Affiliated Colleges：

计算机与大数据学院、软件学院本学院/部未明确归属的数据

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