The following publications are possibly variants of this publication:
- Classification of Alzheimer's Disease by Combination of Convolutional and Recurrent Neural Networks Using FDG-PET ImagesManhua Liu, Danni Cheng, Weiwu Yan, Alzheimer's Disease Neuroimaging Initiative. fini, 2018:35, 2018. [doi]
- Fruit category classification via an eight-layer convolutional neural network with parametric rectified linear unit and dropout techniqueShuihua Wang, Yi Chen. mta, 79(21-22):15117-15133, 2020. [doi]
- Abnormal breast identification by nine-layer convolutional neural network with parametric rectified linear unit and rank-based stochastic poolingYudong Zhang, Chichun Pan, Xianqing Chen, Fubin Wang. jocs, 27:57-68, 2018. [doi]
- Alzheimer's disease diagnosis based on multiple cluster dense convolutional networksFan Li, Manhua Liu, Alzheimer's Disease Neuroimaging Initiative. cmig, 70:101-110, 2018. [doi]
- Deep learning neural networks for emotion classification from text: enhanced leaky rectified linear unit activation and weighted lossHui Yang, Abeer Alsadoon, P. W. C. Prasad, Thair Al-Dala'in, Tarik A. Rashid, Angelika Maag, Omar Hisham Alsadoon. mta, 81(11):15439-15468, 2022. [doi]
- Monte Carlo Ensemble Neural Network for the diagnosis of Alzheimer's diseaseChaoqiang Liu, Fei Huang, Anqi Qiu, Alzheimer's Disease Neuroimaging Initiative. NN, 159:14-24, February 2023. [doi]