Enhanced Grey Wolf Optimization based Hyper-parameter optimized Convolution Neural Network for Kidney Image Classification
Article Sidebar
Main Article Content
Abstract
Over the last few years, Convolution Neural Networks (CNN) have shown dominant performance over real world applications due to their ability to find good solutions and deal with image data. However their performance is highly dependent on the network architecture and methods for optimizing their hyper parameters especially number and size of filters. Designing a good CNN architecture requires human expertise and domain knowledge. So, it is difficult in CNN to find sufficient number and size of filters for classification problems. The standard GWO algorithm used for any optimization purpose suffers from some issues such as slow convergence speed, trapping in local minima and unable to maintain balance between exploration and exploitation. In order to have proper balance between these phases, two modifications in GWO are introduced in this paper. A technique for finding optimum CNN architecture using methods based on Enhanced Grey Wolf Optimization (E-GWO) is proposed. The paper presents optimization of hyper parameters (numbers and size of filters in convolution layer) of CNN using E-GWO to improve the performance of the model. Kidney ultrasound images dataset collected from ultrasound centre is used to evaluate the performance of the proposed algorithm. Experimental results showed that optimization of CNN with E-GWO outperformed CNN optimized with traditional GA, PSO and GWO and conventional CNN yielding 97.01% accuracy. At last, the obtained results are statistically validated using t-test.
Article Details
References
S. M. Anwar et al., “Medical Image Analysis using Convolutional Neural Networks: A Review,” Journal of Medical Systems, vol. 42, no. 11, pp. 1-13, 2018, https://doi.org/10.1007/s10916-018-1088-1
S. S. Yadav, and S. M. Jadhav, “Deep Convolutional Neural Network based Medical Image Classification for Disease Diagnosis,” Journal of Big Data, vol. 6, no. 1, pp. 1-18, 2019, https://doi.org/10.1186/s40537-019-0276-2
K. Fukushima, “A Self-Organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position,” Biological Cybernetics, vol. 36, pp. 193-202, 1980, https://doi.org/10.1007/BF00344251
P. Sermanet, S. Chintala, and Y. LeCun, “Convolutional Neural Networks Applied to House Numbers Digit Classification,” In Proceedings of the 21st international conference on pattern recognition (ICPR2012), IEEE, pp. 3288-3291, Nov. 2012
E. Byla, and W. Pang, “Deepswarm: Optimizing Convolutional Neural Networks Using Swarm Intelligence,” In UK Workshop on Computational Intelligence, Springer, pp. 119-130, Sept. 2019, https://doi.org/10.1007/978-3-030-29933-0_10
Q. Zheng, X. Tian, N. Jiang, and M. Yang, “Layer-wise Learning based Stochastic Gradient Descent Method for the Optimization of Deep Convolutional Neural Network,” Journal of Intelligent & Fuzzy Systems, vol. 37, no. 4, pp. 5641-5654, 2019, 10.3233/JIFS-190861
A. Ratre, “Stochastic Gradient Descent–Whale Optimization Algorithm-based Deep Convolutional Neural Network to Crowd Emotion Understanding,” The Computer Journal, vol. 63, no. 2, pp. 267-282, 2020, https://doi.org/10.1093/comjnl/bxz103
D. O. Melinte, and L. Vladareanu, “Facial Expressions Recognition for Human–Robot Interaction Using Deep Convolutional Neural Networks with Rectified Adam Optimizer,” Sensors, vol. 20, no. 8, pp. 2393, 2020, https://doi.org/10.3390/s20082393
Q. Zhang et al., “Optimization of Culture Conditions for Differentiation of Melon Based on Artificial Neural Network and Genetic Algorithm,” Scientific Reports, vol. 10, no. 1, pp. 1-8, 2020, doi: 10.1038/s41598-020-60278-x
Y. Sun et al., “Automatically Designing CNN Architectures Using the Genetic Algorithm for Image Classification,” IEEE Transactions on Cybernetics, vol. 50, no. 9, pp. 3840-3854, 2020, 10.1109/TCYB.2020.2983860
J. Jayanthi et al., “An Intelligent Particle Swarm Optimization with Convolutional Neural Network for Diabetic Retinopathy Classification Model,” Journal of Medical Imaging and Health Informatics, vol. 11, no. 3, pp. 803-809 2021, https://doi.org/10.1166/jmihi.2021.3362
A. Shahzad et al., “Categorizing White Blood Cells by Utilizing Deep Features of Proposed 4B-AdditionNet-based CNN Network with Ant Colony Optimization,” Complex & Intelligent Systems, pp. 1-17, 2021, https://doi.org/10.1007/s40747-021-00564-x
S. Pathan, P. C. Siddalingaswamy, and T. Ali, “Automated Detection of Covid-19 from Chest X-ray Scans Using an Optimized CNN Architecture,” Applied Soft Computing, vol. 104, pp. 107238, 2021, https://doi.org/10.1016/j.asoc.2021.107238
K. V. S. S. R. Krishna et al., “Classification of Glaucoma Optical Coherence Tomography (OCT) Images -Based on Blood Vessel Identification Using CNN and Firefly Optimization,” Traitement du Signal, vol. 38, no. 1, 2021,
M. Elhoseny, and K. Shankar, “Optimal Bilateral Filter and Convolutional Neural Network based Denoising Method of Medical Image Measurements,” Measurement, vol. 143, pp. 125-135, 2019, https://doi.org/10.1016/j.measurement.2019.04.072
L. Zhang, H. J. Gao, J. Zhang, and B. Badami, “Optimization of the Convolutional Neural Networks for Automatic Detection of Skin Cancer,” Open Medicine, vol. 15, no. 1, pp. 27-37, 2019, doi: 10.1515/med-2020-0006.
T. V. Roshini et al., “Automatic Diagnosis of Diabetic Retinopathy with the Aid of Adaptive Average Filtering with Optimized Deep Convolutional Neural Network,” International Journal of Imaging Systems and Technology, vol. 30, no. 4, pp. 1173-1193, 2020, https://doi.org/10.1002/ima.22419
R. Mohakud, and R. Dash, “Designing A Grey Wolf Optimization Based Hyper-parameter Optimized Convolutional Neural Network Classifier for Skin Cancer Detection,” Journal of King Saud University-Computer and Information Sciences, 2021, https://doi.org/10.1016/j.jksuci.2021.05.012
R. Ahmadi, G. Ekbatanifard, and P. Bayat, “A Modified Grey Wolf Optimizer Based Data Clustering Algorithm,” Applied Artificial Intelligence, vol. 35, no. 1, pp. 63-79, 2021, https://doi.org/10.1080/08839514.2020.184210
V. Kumar, and D. Kumar, “An Astrophysics-inspired Grey Wolf Algorithm for Numerical Optimization and its Application to Engineering Design Problems,” Advances in Engineering Software, vol. 112, pp. 231-254, 2017, https://doi.org/10.1016/j.advengsoft.2017.05.008
K. Luo, “Enhanced Grey Wolf Optimizer With a Model for Dynamically Estimating the Location of the Prey,” Applied Soft Computing, vol. 77, pp. 225-235, 2019, https://doi.org/10.1016/j.asoc.2019.01.025
W. Long, J. Jiao, X. Liang, and M. Tang, “An Exploration-enhanced Grey Wolf Optimizer to Solve High-dimensional Numerical Optimization,” Engineering Applications of Artificial Intelligence, vol. 68, pp. 63-80, 2018, https://doi.org/10.1016/j.engappai.2017.10.024
E. S. M. El-Kenawy, M. M. Eid, M. Saber, and A. Ibrahim, “MbGWO-SFS: Modified Binary Grey Wolf Optimizer Based on Stochastic Fractal Search for Feature Selection,” IEEE Access, vol. 8, pp. 107635-107649, 2020, 10.1109/ACCESS.2020.3001151
N. Mittal, U. Singh, and B. S. Sohi, “Modified Grey Wolf Optimizer for Global Engineering Optimization,” Applied Computational Intelligence and Soft Computing, 2016, https://doi.org/10.1155/2016/7950348
H. Joshi, and S. Arora, “Enhanced Grey Wolf Optimization Algorithm for Global Optimization,” Fundamenta Informaticae, vol. 153, no. 3, pp. 235-264, 2017, 10.3233/FI-2017-1539
Priyanka, and D. Kumar, “Kidney Image Classification Using Transfer Learning With Convolutional Neural Network,” International Journal of Computational Vision and Robotics, vol. 12, no. 6, pp. 595-613, 2022, https://doi.org/10.1504/IJCVR.2022.126499
Y. Sun, B. Xue, M. Zhang, and G. G. Yen, “Completely Automated CNN Architecture Design Based on Blocks,” IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 4, pp. 1242-1254, 2019, 10.1109/TNNLS.2019.2919608
M. Pradhan, P. K. Roy, and T. Pal, “Grey Wolf Optimization Applied to Economic Load Dispatch Problems,” International Journal of Electrical Power & Energy Systems, vol. 83, pp. 325-334, 2016, https://doi.org/10.1016/j.ijepes.2016.04.034