An Optimization Method for Tuning Hyper-Parameters of SGAN with Ensemble Classification Regression Model
| International Journal of Electrical and Electronics Engineering |
| © 2023 by SSRG - IJEEE Journal |
| Volume 10 Issue 4 |
| Year of Publication : 2023 |
| Authors : Valarmathi Srinivasan, Vijayabhanu Rajagopal |
10.14445/23488379/IJEEE-V10I4P103
How to Cite?
Valarmathi Srinivasan, Vijayabhanu Rajagopal, "An Optimization Method for Tuning Hyper-Parameters of SGAN with Ensemble Classification Regression Model," SSRG International Journal of Electrical and Electronics Engineering, vol. 10, no. 4, pp. 24-36, 2023. Crossref, https://doi.org/10.14445/23488379/IJEEE-V10I4P103
Abstract:
Diabetic Retinopathy (DR) is a potential condition of Diabetes Mellitus (DM) that causes lesions on the retina, reducing vision and perhaps leading to disability if not properly treated. A Special Generative Adversarial Network with Ensemble Classification Regression (SGAN-ECR) model was introduced for the effective categorisation of various DR grades with the generation of high-contrast and low-saturated RF images. However, the Multi-Scale Attention Residual Network with Gradient Boosting (MSA-ResNet-GB) model used in SGAN-ECR requires manually assigning values for many hyperparameters. An inappropriate value of the hyper-parameter leads to an increased error rate. This paper presents a method using the Enhanced Mine Blast Algorithm (EMBA) for selecting optimal hyper-parameters of MSA-ResNet-GB, such as the number of convolution layers, number of filters, filter size, number of Fully Connected (FC) layers and the hidden units in the FC layer to improve the architecture. Essential principles of EMBA are derived from the mine bomb explosion in real-time applications. The initial population of shrapnel fragments represents the initial hyper-parameter, and the computation of their subsequent locations represents the search for the best hyper-parameter. SGAN-ECR with parameter-optimised MSA-ResNet is named SGAN-OECR. In SGAN-OECR, optimised MSA-ResNet is trained by training images, and then the trained model is used to recognise the abnormalities in test images. MLP classifiers in the last FC layers of MSA-ResNet classify the severity of DR lesions. Finally, the experimental results of SGAN-OECR on Kaggle-APTOS and IDRiD datasets have 99.03% and 98.63% of accuracy, respectively, which is higher than the existing DR classification models.
Keywords:
Diabetic retinopathy, Generative adversarial network, Hyper-parameters, Mine blast algorithm, Optimisation, Retinal fundus images.
References:
[1] Dóra Júlia Eszes et al., "Diabetic Retinopathy Screening in Patients with Diabetes Using a Handheld Fundus Camera: The Experience from the South-Eastern Region in Hungary," Journal of Diabetes Research, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Solomon and Goldberg, "ETDRS Grading of Diabetic Retinopathy: Still The Gold Standard?,” Ophthalmic Research, vol. 62, no. 4, pp. 190-195, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Wei Zhou, Chengdong Wu, and Xiaosheng Yu, "Computer-Aided Diagnosis for Diabetic Retinopathy Based on Fundus Image," 2018 37th Chinese Control Conference (CCC), IEEE, pp. 9214-9219, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Meteb Altaf et al., "Monitoring Diabetic Foot by Designing a New Smart Sole," SSRG International Journal of Pharmacy and Biomedical Engineering, vol. 7, no. 3, pp. 15-20, 2020.
[CrossRef] [Publisher Link]
[5] Nabila Eladawi et al., "Computer-Aided Diagnosis System Based on a Comprehensive Local Features Analysis for Early Diabetic Retinopathy Detection using OCTA," Diabetes and Fundus OCT, pp. 1-23, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[6] K.S. Sreejini, and V.K. Govindan, "Retrieval of Pathological Retina Images Using Bag of Visual Words and Plsa Model," Engineering Science and Technology, An International Journal, vol. 22, no. 3, pp. 777-785, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Gwenolé Quellec et al., "Automatic Detection of Referral Patients due to Retinal Pathologies through Data Mining," Medical Image Analysis, 29, pp 47-64, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Ping-Nan Chen et al., "General Deep Learning Model for Detecting Diabetic Retinopathy," BMC Bioinformatics, vol. 22, no. 5, pp. 1- 15, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Zhentao Gao et al., "Diagnosis of Diabetic Retinopathy Using Deep Neural Networks," IEEE Access, vol. 7, pp. 3360-3370, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Ms.S.Deepa, and Mr.S.Vijayprasath, "Certain Investigation of the Retinal Hemorrhage Detection in Fundus Images," SSRG International Journal of Electronics and Communication Engineering, vol. 2, no. 2, pp. 24-34, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Mustapha Aatila et al., "Diabetic Retinopathy Classification Using Resnet50 and VGG-16 Pretrained Networks," International Journal of Computer Engineering and Data Science, vol. 1, no. 1, pp. 1-7, 2021.
[Google Scholar] [Publisher Link]
[12] Yunpeng Chen et al., "Dual Path Networks," Advances in Neural Information Processing Systems, vol. 30, 2017.
[Google Scholar]
[13] Fahman Saeed, Muhammad Hussain, and Hatim A. Aboalsamh, "Automatic Diabetic Retinopathy Diagnosis using Adaptive Fine-Tuned Convolutional Neural Network," IEEE Access, vol. 9, pp. 41344-41359, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[14] S.Bibiana Vincy, and Dr. C. Chitra, "Detection of Exudates in Color Fundus Image," SSRG International Journal of Electronics and Communication Engineering, vol. 2, no. 11, pp. 1-10, 2015.
[CrossRef] [Publisher Link]
[15] Valarmathi Srinivasan, and Vijayabhanu Rajagopal, "Multi-Scale Attention-Based Mechanism in Gradient Boosting Convolutional Neural Network for Diabetic Retinopathy Grade Classification," International Journal of Intelligent Engineering and System, vol. 15, no. 4, pp. 489-498, 2022.
[CrossRef] [Google Scholar]
[16] Jordi de la Torre, Aida Valls, and Domenec Puig, “A Deep Learning Interpretable Classifier for Diabetic Retinopathy Disease Grading," Neurocomputing, vol. 396, pp. 465-476, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[17] D. Jude Hemanth, Omer Deperlioglu, and Utku Kose, "An Enhanced Diabetic Retinopathy Detection and Classification Approach Using Deep Convolutional Neural Network," Neural Computing and Applications, vol. 32, no. 3, pp. 707-721, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Niloy Sikder et al., "Severity Classification of Diabetic Retinopathy Using an Ensemble Learning Algorithm through Analysing Retinal Images," Symmetry, vol. 13, no. 4, pp. 1-26, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Mohammed Al-Mijalli, "Hemodynamic in Human Diabetic Abdominal Aneurysmal Aorta Using Computational Fluid Dynamics," SSRG International Journal of Pharmacy and Biomedical Engineering, vol. 8, no. 2, pp. 1-5, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Anas Bilal et al., "Diabetic Retinopathy Detection and Classification Using Mixed Models for a Disease Grading Database," IEEE Access, vol. 9, pp. 23544-23553, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Zubair Khan et al., "Diabetic Retinopathy Detection Using VGG-NIN a Deep Learning Architecture," IEEE Access, vol. 9, pp. 61408- 61416, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Eman Abdelmaksoud et al., "Automatic Diabetic Retinopathy Grading System Based on Detecting Multiple Retinal Lesions,” IEEE Access, vol. 9, pp. 15939-15960, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Hassan Tariq et al., "Performance Analysis of Deep-Neural-Network-Based Automatic Diagnosis of Diabetic Retinopathy," Sensors, vol. 22, no. 1, pp. 1-15, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[24] B. Venkaiahppalaswamy, PVGD Prasad Reddy, and Suresh Batha, "An Effective Diagnosis of Diabetic Retinopathy Based on 3d Hybrid Squeezenet Architecture," International Journal of Engineering Trends and Technology, vol. 70, no. 12, pp. 147-159, 2022.
[CrossRef] [Publisher Link]
[25] Alnur Alimanov, and Md Baharul Islam, "Retinal Image Restoration and Vessel Segmentation using Modified Cycle-CBAM and CBAM-Unet," 2022 Innovations in Intelligent Systems and Applications Conference (ASYU), IEEE, pp. 1-6, 2022.
[Google Scholar]
[26] Zhaomin Yao et al., "Funswin: A Deep Learning Method to Analysis Diabetic Retinopathy Grade and Macular Edema Risk Based on Fundus Images," Frontiers in physiology, 1462, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Najib Ullah et al., "Diabetic Retinopathy Detection Using Genetic Algorithm-Based CNN Features and Error Correction Output Code SVM Framework Classification Model,” Wireless Communications and Mobile Computing, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Muhammad Mohsin Butt et al., "Diabetic Retinopathy Detection from Fundus Images of the Eye Using Hybrid Deep Learning Features,” Diagnostics, vol. 12, no. 7, p. 1607, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[29] APTOS 2019 Blindness Detection Dataset. [Online]. Available: https://www.kaggle.com/c/aptos2019-blindness-detection/
[30] Prasanna Porwal et al., "Indian Diabetic Retinopathy Image Dataset (Idrid): A Database for Diabetic Retinopathy Screening Research," Data, vol. 3, no. 3, p. 25, 2018.
[CrossRef] [Google Scholar] [Publisher Link]