Browse > Article
http://dx.doi.org/10.9717/kmms.2020.23.9.1129

Optimization of Deep Learning Model Using Genetic Algorithm in PET-CT Image Alzheimer's Classification  

Lee, Sanghyeop (Dept. of Electronic Eng., Kyungsung University)
Kang, Do-Young (Dept, of Nuclear Medicine, Donga University College of Medicine)
Song, Jongkwan (Dept. of Electronic Eng., Kyungsung University)
Park, Jangsik (Dept. of Electronic Eng., Kyungsung University)
Publication Information
Journal of Korea Multimedia Society / v.23, no.9, 2020 , pp. 1129-1138 More about this Journal
Abstract
The performance of convolutional deep learning networks is generally determined according to parameters of target dataset, structure of network, convolution kernel, activation function, and optimization algorithm. In this paper, a genetic algorithm is used to select the appropriate deep learning model and parameters for Alzheimer's classification and to compare the learning results with preliminary experiment. We compare and analyze the Alzheimer's disease classification performance of VGG-16, GoogLeNet, and ResNet to select an effective network for detecting AD and MCI. The simulation results show that the network structure is ResNet, the activation function is ReLU, the optimization algorithm is Adam, and the convolution kernel has a 3-dilated convolution filter for the accuracy of dementia medical images.
Keywords
Alzheimer's Disease Classification; Genetic Algorithm; Deep Learning; ResNet;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
1 V. Nair and G.E. Hinton, "Rectified Linear Units Improve Restricted Boltzmann Machines," Proceedings of International Conference on Machine Learning, pp. 807-814, 2010.
2 A.L. Maas, A.Y. Hannun, and A.Y. Ng, "Rectifier Nonlinearities Improve Neural Network Acoustic Models," Proceedings of International Conference on Machine Learning, pp. 3-9, 2013.
3 A. Hannun, C. Case, J. Casper, B. Catanzaro, G. Diamos, E. Elsen, et al., "Deep Speech: Scaling Up End-to-end Speech Recognition," arXiv Preprint arXiv:1412.5567, 2014.
4 K.P. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, Cambridge, Massa., 2012.
5 D.P. Kingma and J. Ba, "Adam: A Method for Stochastic Optimization," arXiv Preprint arXiv: 1412.6980, 2014.
6 C.F. Geoffrey and N. Mansour, "A Hybrid Genetic Algorithm for Task Allocation in Multicomputers," Proceedings of International Conference on Genetic Algorithms, pp. 446-473, 1991.
7 W.M. Spears, "Crossover or Mutation?," Foundations of Genetic Algorithms, Vol. 2, No. 1, pp. 221-337, 1993.
8 Z.W. Geem, J.H. Kim, and G.V. Loganathan, "A New Heuristic Optimization Algorithm: Harmony Search," SIMULATION: Transactions of The Society for Modeling and Simulation International, Vol. 76, No. 2, pp. 60-68, 2001.   DOI
9 L.D. Whitley, "The GENITOR Algorithm and Selection Pressure: Why Rank-based Allocation of Reproductive Trials is Best," Proceedings of International Conference on Genetic Algorithms, pp. 116-123, 1989.
10 S. Misciagna, "Basic PET Data Analysis Techniques," Positron Emission Tomography-Recent Developments in Instrumentation, Research and Clinical Oncological Practice, IntechOpen, London, UK, 2013.
11 H.J. Park, "Methodological Review on Functional Neuroimaging Using Positron Emission Tomography," Nuclear Medicine and Molecular Imaging, Vol. 41, No. 2, pp. 71-77, 2007.
12 H.I. Suk, S.W. Lee, and D. Shen, "Hierarchical Feature Representation and Multimodal Fusion with Deep Learning for AD/MCI Diagnosis," Neuro Image, Vol. 101, No. 1, pp. 569-582, 2014.
13 S.G. Hwang and H.K. Park, "An Analysis on Prescribing Patterns of Alzheimer's Dementia Treatment and Choline Alfoscerate Using HIRA Claims Data," Korean Journal of Clinical Pharmacy, Vol. 29, No. 1, pp. 1-8, 2019.   DOI
14 H.I. Suk and D. Shen, "Deep Learning-based Feature Representation for AD/MCI Classification," Medical Image Computing and Computer-Assisted Intervention, Vol. 8150, No. 1, pp. 583-590, 2013.
15 R. Li, W. Zhang, H.I. Suk, W. Li, J, Li, D. Shen, et al., "Deep Learning Based Imaging Data Completion for Improved Brain Disease Diagnosis," Medical Image Computing and Computer-Assisted Intervention, Vol. 8675, No. 1, pp. 305-312, 2014.
16 C. Carlos and M. Silveira, "Classification of Alzheimer's Disease from FDG-PET Images Using Favourite Class Ensembles," Proceeding of 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2477-2480, 2013.
17 J. Chen, L. Yang, Y. Zhang, M. Alber, and D.Z. Chen, "Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation," Proceeding of Advances in Neural Information Processing Systems, pp. 3036- 3044, 2016.
18 I. Gariali, M. Adel, S. Bourennane, and E. Guedj, "Region-based Brain Selection and Classification on Pet Images for Alzheimer's Disease Computer Aided Diagnosis," Proceeding of IEEE International Conference on Image Processing, pp. 1473-1477, 2015.
19 S.Y. Kwon, Y.J. Kim, and G.G. Kim, "An Automatic Breast Mass Segmentation Based on Deep Learning on Mammogram," Journal of Korea Multimedia Society, Vol. 21, No. 12, pp. 1363-1369, 2018.   DOI
20 S. Liu, S. Liu, W. Cai, S. Pujol, R. Kikinis, and D. Feng, "Early Diagnosis of Alzheimer's Disease with Deep Learning," Proceeding of IEEE International Symposium on Biomedical Imaging, pp. 1015-1018, 2014.
21 X. Zhang, J. Zou, K. He, and J. Sun, "Accelerating Very Deep Convolutional Networks for Classification and Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 38, No. 10, pp. 1943-1955, 2015.   DOI
22 K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778, 2016.
23 C. Szegedy, W. Liu, Y. Ji, P. Sermanet, S. Reed, D. Anguelov, et al., "Going Deeper with Convolutions," Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-9, 2015.