Detection of abnormal brain in MRI via improved AlexNet and ELM optimized by chaotic bat algorithm

• Published in: Neural computing & applications Vol. 33; no. 17; pp. 10799 - 10811
• Main Authors: Lu, Siyuan, Wang, Shui-Hua, Zhang, Yu-Dong
• Format: Journal Article
• Language: English
• Published: London Springer London 01.09.2021; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Alzheimer's disease; Artificial Intelligence; Artificial neural networks; Automation; Brain; Brain cancer; Brain diseases; Classification; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Deep learning; Discriminant analysis; Experiments; Feature selection; Image Processing and Computer Vision; Machine learning; Magnetic resonance imaging; Medical imaging; Medical research; Neural networks; Probability and Statistics in Computer Science; S. I : Hybridization of Neural Computing with Nature Inspired Algorithms; Signal processing; Special Issue on Hybridization between Neural Computing and Nature Inspired Algorithms for Solving Multi-Criteria Decision-Making Problems; Support vector machines; Tumors; Wavelet transforms; Deep learning; AlexNet; Extreme learning machine; Magnetic resonance image; Computer-aided diagnosis

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 w...