An Effective Diagnosis of Diabetic Retinopathy Based on 3d Hybrid Squeezenet Architecture

An Effective Diagnosis of Diabetic Retinopathy Based on 3d Hybrid Squeezenet Architecture

© 2022 by IJETT Journal
Volume-70 Issue-12
Year of Publication : 2022
Author : B. Venkaiahppalaswamy, PVGD Prasad Reddy, Suresh Batha
DOI : 10.14445/22315381/IJETT-V70I12P216

How to Cite?

B. Venkaiahppalaswamy, PVGD Prasad Reddy, 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,

Diabetic retinopathy (DR), which affects an increasing number of people of all ages who have the disease, causes vision issues. Computer-aided diagnosis technology, particularly for the development of deep learning techniques, has become widely used in research for the screening of DR. Accurate annotation is more expensive than other vision tasks since most deep learning-based DR grading models need many annotations to provide data direction, and it is difficult for professionals to detect tiny lesion locations from fundus images. This paper developed a deep learning-based 3D hybrid squeezenet architecture for multiclass DR classification. Here, 3D CNN and squeeznet are hybridized to detect the classes such as mild, moderate proliferate accurately, and severe DR. In addition, an effective data augmentation/ enhancement is achieved using Gaussian blurring and random shift, as well as their combination. The proposed system for data augmentation in the initial stage is to lessen the overfitting issue in 3D hybrid squeeznet to acquire a more trustworthy and robust classification. In this case, intend to categorize several classes, and constructing a 3-D based hybrid network is a superior design since it will train the data more effectively and with less complexity. Finally, a performance study with and without data augmentation is performed to assess the efficiency of the proposed design. The experimental results of the proposed methodology attain enhanced performance than the different compared techniques in terms of accuracy, precision, recall, F1 score, sensitivity, specificity, kappa score and run time.

Diabetic retinopathy, Deep learning, Gaussian blurring, Random shifting, Data augmentation.

[1] Ashwini Tuppad, and Shantala Devi Patil, "Machine Learning for Diabetes Clinical Decision Support: A Review," Advances in Computational Intelligence, vol. 2, no. 2, pp. 1-24, 2022. Crossref,
[2] Thippa Reddy Gadekallu et al., "Early Detection of Diabetic Retinopathy Using PCA-Firefly Based Deep Learning Model," Electronics, vol. 9, no. 2, p. 274, 2020. Crossref,
[3] Shelley Spurr et al., "The Prevalence of Undiagnosed Prediabetes/Type 2 Diabetes, Prehypertension/Hypertension and Obesity Among Ethnic Groups of Adolescents in Western Canada," BMC Pediatrics, vol. 20, no. 1, pp. 1-9, 2020. Crossref,
[4] Raphael D. Ayivi et al., "Lactic Acid Bacteria: Food Safety and Human Health Applications," Dairy, vol. 1, no. 3, pp. 202-232, 2020. Crossref,
[5] Irini Chatziralli, "Ranibizumab for the Treatment of Diabetic Retinopathy," Expert Opinion on Biological Therapy, vol. 21, no. 8, pp. 991-997, 2021. Crossref,
[6] Prawej Ansari et al., "Diabetic Retinopathy: An Overview on Mechanisms, Pathophysiology and Pharmacotherapy," Diabetology, vol. 3, no. 1, pp. 159-175, 2022. Crossref,
[7] N. Durga, Dr. D. Kerana Hanirex, and Dr. A. Muthukumaravel, "A Systematic Review on Diabetic Retinopathy and Common Eye Diseases Detection through Deep Learning Techniques," Journal of Positive School Psychology, vol. 6, no. 4, pp. 1905-1919, 2022.
[8] Stela Vujosevic et al., "Screening for Diabetic Retinopathy: New Perspectives and Challenges," The Lancet Diabetes & Endocrinology, vol. 8, no. 4, pp. 337-347, 2020. Crossref,
[9] Karthik Kumar et al., "Clinical Features and Surgical Outcomes of Complications of Proliferative Diabetic Retinopathy in Young Adults with Type 1 Diabetes Mellitus Versus Type 2 Diabetes Mellitus-A Comparative Observational Study," Indian Journal of Ophthalmology, vol. 69, no. 11, pp. 3289-3295, 2021. Crossref,
[10] Daniel Yim et al., "Barriers in Establishing Systematic Diabetic Retinopathy Screening through Telemedicine in Low-and MiddleIncome Countries," Indian Journal of Ophthalmology, vol. 69, no. 11, pp. 2987-2992, 2021. Crossref,
[11] Charles C Wykoff et al., "Risk of Blindness Among Patients with Diabetes and Newly Diagnosed Diabetic Retinopathy," Diabetes Care, vol. 44, no. 3, pp. 748-756, 2021. Crossref,
[12] Emma Beede et al., "A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy," Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1-12. 2020. Crossref,
[13] Ursula Schmidt-Erfurth et al., "AI-Based Monitoring of Retinal Fluid in Disease Activity and Under Therapy," Progress in Retinal and Eye Research, vol. 86, p. 100972, 2022. Crossref,
[14] Christian Enders et al., "Comparison Between Findings in Optical Coherence Tomography Angiography and in Fluorescein Angiography in Patients with Diabetic Retinopathy," Ophthalmologica, vol. 243, no. 1, pp. 21-26, 2020. Crossref,
[15] Md Mohaimenul Islam et al., "Deep Learning Algorithms for Detection of Diabetic Retinopathy in Retinal Fundus Photographs: A Systematic Review and Meta-Analysis," Computer Methods and Programs in Biomedicine, vol. 191, p. 105320, 2020. Crossref,
[16] Isabella Castiglioni et al., "AI Applications to Medical Images: From Machine Learning to Deep Learning,” Physica Medica, vol. 83, pp. 9-24, 2021. Crossref,
[17] Julia Amann et al., "Explainability for Artificial Intelligence in Healthcare: A Multidisciplinary Perspective," BMC Medical Informatics and Decision Making, vol. 20, no. 1, pp. 1-9, 2020. Crossref,
[18] Sujata Chaudhari et al., "Yolo Real Time Object Detection," International Journal of Computer Trends and Technology, vol. 68, no. 6, pp. 70-76, 2020. Crossref,
[19] Imran Qureshi, Jun Ma, and Qaisar Abbas, "Diabetic Retinopathy Detection and Stage Classification in Eye Fundus Images Using Active Deep Learning," Multimedia Tools and Applications, vol. 80, no. 8, pp. 11691-11721, 2021. Crossref,
[20] V Desika Vinayaki, and R Kalaiselvi, "Multithreshold Image Segmentation Technique using Remora Optimization Algorithm for Diabetic Retinopathy Detection from Fundus Images," Neural Processing Letters, vol. 54, no. 3, pp. 2363-2384, 2022. Crossref,
[21] J. Granty Regina Elwin et al., "Ar-HGSO: Autoregressive-Henry Gas Sailfish Optimization Enabled Deep Learning Model for Diabetic Retinopathy Detection and Severity Level Classification," Biomedical Signal Processing and Control, vol. 77, p. 103712, 2022. Crossref,
[22] S.Supraja, and P.Ranjith Kumar, "An Intelligent Traffic Signal Detection System Using Deep Learning," SSRG International Journal of VLSI & Signal Processing, vol. 8, no. 1, pp. 5-9, 2021. Crossref,
[23] Eman Abdel Maksoud, Sherif Barakat, and Mohammed Elmogy, "A Computer-Aided Diagnosis System for Detecting Various Diabetic Retinopathy Grades Based on a Hybrid Deep Learning Technique," Medical & Biological Engineering & Computing, pp. 2015–2038, 2022. Crossref,
[24] Salisu Ibrahim et al., "Diabetic Retinopathy Detection using Local Extrema Quantized Haralick Features with Long Short-Term Memory Network," International Journal of Biomedical Imaging, vol. 2021, 2021. Crossref,
[25] Lakshmana Kumar Ramasamy et al., "Detection of Diabetic Retinopathy using a Fusion of Textural and Ridgelet Features of Retinal Images and Sequential Minimal Optimization Classifier," PeerJ Computer Science, vol. 7, p. e456, 2021. Crossref,
[26] Muhammad Kashif Yaqoob et al., "Resnet Based Deep Features and Random Forest Classifier for Diabetic Retinopathy Detection," Sensors, vol. 21, no. 11, p. 3883, 2021. Crossref,
[27] Hemavathi S, and Dr.S. Padmapriya, “Detection of Diabetic Retinopathy on Retinal Images using Support Vector Machine,” SSRG International Journal of Computer Science and Engineering – Special Issue ICMR, pp. 5-8, 2019.
[28] Dr. Shubhangi D C, and Tasleem Begum, “Diagnosis of Diabetic Retinopathy Using Dimensional Reduction Algorithm,” International Journal of Engineering Trends and Technology, pp. 178-181, vol. 67, no. 10, pp. 178-181, 2019. Crossref,
[29] S. Regina Lourdhu Suganthi, U K Sneha, and Shwetha S, “Diabetic Retinopathy Classification Using Machine Learning Techniques,” International Journal of Engineering Trends and Technology, vol. 68, no. 1, pp. 51-56, 2020. Crossref,
[30] Manojkumar S. B, and Sheshadri H. S, “Analysis of Detection of Diabetic Retinopathy using LPB and Deep Learning Techniques,” International Journal of Engineering Trends and Technology, vol. 68, no. 12, pp. 123-131, 2020. Crossref,
[31] Yue Miao, and Siyuan Tang, “Classification of Diabetic Retinopathy Based on Multiscale Hybrid Attention Mechanism and Residual Algorithm,” Wireless Communications and Mobile Computing, vol. 2022, 2022. Crossref,
[32] Santiago Toledo-Cortés et al., “Grading Diabetic Retinopathy and Prostate Cancer Diagnostic Images with Deep Quantum Ordinal Regression,” Computers in Biology and Medicine, vol. 145, p. 105472, 2022. Crossref,
[33] Ambaji S. Jadhav, Pushpa B. Patil, and Sunil Biradar, “Optimal Feature Selection-Based Diabetic Retinopathy Detection Using Improved Rider Optimization Algorithm Enabled with Deep Learning,” Evolutionary Intelligence, vol. 14, no. 4, pp. 1431-1448, 2021. Crossref,
[34] Feng Li et al., "Deep Learning-Based Automated Detection for Diabetic Retinopathy and Diabetic Macular Oedema in Retinal Fundus Photographs," Eye, vol. 36, no. 7, pp. 1433-1441, 2022. Crossref,
[35] 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,