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http://dx.doi.org/10.9717/kmms.2020.24.4.519

Genetic algorithm based deep learning neural network structure and hyperparameter optimization  

Lee, Sanghyeop (Dept. of Electronic Eng., Kyungsung University)
Kang, Do-Young (Dept, of Nuclear Medicine, Donga University College of Medicine)
Park, Jangsik (Dept. of Electronic Eng., Kyungsung University)
Publication Information
Journal of Korea Multimedia Society / v.24, no.4, 2021 , pp. 519-527 More about this Journal
Abstract
Alzheimer's disease is one of the challenges to tackle in the coming aging era and is attempting to diagnose and predict through various biomarkers. While the application of various deep learning-based technologies as powerful imaging technologies has recently expanded across the medical industry, empirical design is not easy because there are various deep earning neural networks architecture and categorical hyperparameters that rely on problems and data to solve. In this paper, we show the possibility of optimizing a deep learning neural network structure and hyperparameters for Alzheimer's disease classification in amyloid brain images in a representative deep earning neural networks architecture using genetic algorithms. It was observed that the optimal deep learning neural network structure and hyperparameter were chosen as the values of the experiment were converging.
Keywords
Alzheimer's disease; Deep learning; Convolutional neural network; Genetic Algorithm; PET/CT; FBB; Brain imaging;
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