An Effective Heuristic Optimizer with Deep Learning-assisted Diabetic Retinopathy Diagnosis on Retinal Fundus Images

Authors

  • Cinnappan Nithyeswari Department of Computer Science, Periyar Arts College, India
  • Ganesan Karthikeyan Department of Computer Science, Periyar Arts College, India
Volume: 14 | Issue: 3 | Pages: 14308-14312 | June 2024 | https://doi.org/10.48084/etasr.7004

Abstract

Diabetic Retinopathy (DR), a common diabetes complication affecting retinal blood vessels, may result in vision damage if not addressed promptly. Early and accurate detection is crucial for effective management, and Deep Learning (DL) techniques offer promising tools for the automated screening of Retinal Fundus Images (RFIs). This approach enhances objectivity, reduces inter-observer variability, and has the potential to extend the DR diagnoses to regions with limited access to specialized medical professionals. This manuscript presents the design of the Beluga Whale Optimizer (BWO) with Deep Learning (DL)-assisted DR Diagnosis on RFIs (BWODL-DRDRFI) technique in the Internet of Things (IoT) platform. The proposed technique automatically examines the RFIs for identifying and classifying DR. During the IoT-based data-gathering procedure the patient utilizes a head-mounted camera for capturing the RFI and sends it to a cloud server. Median Filtering (MF)-based image preprocessing is performed to eradicate noise. Next, the BWODL-DRDRFI technique exploits the ShuffleNet-v2 approach to derive feature vectors. For DR recognition, the BWODL-DRDRFI technique applies a deep Stacked AutoEncoder (SAE) model. Finally, the BWO model optimally adjusts the hyperparameter values of the DSAE model for greater classification performance. The simulation output of the BWODL-DRDRFI approach can be examined on a standard image dataset and the outputs are computed on discrete measures. The simulation result highlighted the enhanced performance of the BWODL-DRDRFI approach in the DR diagnosis process.

Keywords:

diabetic retinopathy, Beluga Whale Optimizer (BWO), Retinal Fundus Images (RFIs), deep learning, computer-aided diagnosis

Downloads

Download data is not yet available.

References

R. Vij and S. Arora, "A Systematic Review on Diabetic Retinopathy Detection Using Deep Learning Techniques," Archives of Computational Methods in Engineering, vol. 30, no. 3, pp. 2211–2256, Apr. 2023.

S. Bhandari, S. Pathak, and S. A. Jain, "A Literature Review of Early-Stage Diabetic Retinopathy Detection Using Deep Learning and Evolutionary Computing Techniques," Archives of Computational Methods in Engineering, vol. 30, no. 2, pp. 799–810, Mar. 2023.

S. Suganyadevi, K. Renukadevi, K. Balasamy, and P. Jeevitha, "Diabetic Retinopathy Detection Using Deep Learning Methods," in 2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT), Trichy, India, Feb. 2022.

R. R. Maaliw et al., "An Enhanced Segmentation and Deep Learning Architecture for Early Diabetic Retinopathy Detection," in 2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA, Mar. 2023, pp. 0168–0175.

N. Gundluru et al., "Enhancement of Detection of Diabetic Retinopathy Using Harris Hawks Optimization with Deep Learning Model," Computational Intelligence and Neuroscience, vol. 2022, May 2022, Art. no. e8512469.

S. Yadav and P. Awasthi, "Diabetic Retinopathy Detection Using Deep Learning and Inception-V3 Model," International Research Journal of Modernization in Engineering Technology and Science, vol. 4, no. 6, pp. 1731–1735, Jun. 2022.

T. Usman, Y. Saheed, D. Ignace, and A. Nsang, "Diabetic Retinopathy Detection using Principal Component Analysis Multi-Label Feature Extraction and Classification," International Journal of Cognitive Research in Science Engineering and Education, vol. 4, pp. 78–88, Feb. 2023.

R. Ramesh and S. Sathiamoorthy, "A Deep Learning Grading Classification of Diabetic Retinopathy on Retinal Fundus Images with Bio-inspired Optimization," Engineering, Technology & Applied Science Research, vol. 13, no. 4, pp. 11248–11252, Aug. 2023.

B. Ahmed, G. Ali, A. Hussain, A. Baseer, and J. Ahmed, "Analysis of Text Feature Extractors using Deep Learning on Fake News," Engineering, Technology & Applied Science Research, vol. 11, no. 2, pp. 7001–7005, Apr. 2021.

E. Özbay, "An active deep learning method for diabetic retinopathy detection in segmented fundus images using artificial bee colony algorithm," Artificial Intelligence Review, vol. 56, no. 4, pp. 3291–3318, Apr. 2023.

R. Sebti, S. Zroug, L. Kahloul, and S. Benharzallah, "A Deep Learning Approach for the Diabetic Retinopathy Detection," in Innovations in Smart Cities Applications Volume 5, 2022, pp. 459–469.

A. Ayala, T. Ortiz Figueroa, B. Fernandes, and F. Cruz, "Diabetic Retinopathy Improved Detection Using Deep Learning," Applied Sciences, vol. 11, no. 24, Jan. 2021, Art. no. 11970.

K. Parthiban and M. Kamarasan, "Diabetic retinopathy detection and grading of retinal fundus images using coyote optimization algorithm with deep learning," Multimedia Tools and Applications, vol. 82, no. 12, pp. 18947–18966, May 2023.

Z. Khan et al., "Diabetic Retinopathy Detection Using VGG-NIN a Deep Learning Architecture," IEEE Access, vol. 9, pp. 61408–61416, 2021.

A. K. Gangwar and V. Ravi, "Diabetic Retinopathy Detection Using Transfer Learning and Deep Learning," in Evolution in Computational Intelligence, 2021, pp. 679–689.

K. Shankar, E. Perumal, and R. M. Vidhyavathi, "Deep neural network with moth search optimization algorithm based detection and classification of diabetic retinopathy images," SN Applied Sciences, vol. 2, no. 4, Mar. 2020, Art. no. 748.

Z. Chen, J. Yang, L. Chen, and H. Jiao, "Garbage classification system based on improved ShuffleNet v2," Resources, Conservation and Recycling, vol. 178, Mar. 2022, Art. no. 106090.

Y. Yu, J. Li, J. Li, Y. Xia, Z. Ding, and B. Samali, "Automated damage diagnosis of concrete jack arch beam using optimized deep stacked autoencoders and multi-sensor fusion," Developments in the Built Environment, vol. 14, Apr. 2023, Art. no. 100128.

C. Zhong, G. Li, and Z. Meng, "Beluga whale optimization: A novel nature-inspired metaheuristic algorithm," Knowledge-Based Systems, vol. 251, Sep. 2022, Art. no. 109215.

M. Thirunavukkarasu, H. Lala, and Y. Sawle, "Reliability index based optimal sizing and statistical performance analysis of stand-alone hybrid renewable energy system using metaheuristic algorithms," Alexandria Engineering Journal, vol. 74, pp. 387–413, Jul. 2023.

G. Maffre et al., "Messidor," ADCIS. https://www.adcis.net/en/third-party/messidor/.

K. Shankar, E. Perumal, M. Elhoseny, and P. Nguyen, "An IoT-Cloud Based Intelligent Computer-Aided Diagnosis of Diabetic Retinopathy Stage Classification Using Deep Learning Approach," Computers, Materials & Continua, vol. 66, no. 2, pp. 1665–1680, 2020.

Downloads

How to Cite

[1]
C. Nithyeswari and G. Karthikeyan, “An Effective Heuristic Optimizer with Deep Learning-assisted Diabetic Retinopathy Diagnosis on Retinal Fundus Images”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 3, pp. 14308–14312, Jun. 2024.

Metrics

Abstract Views: 121
PDF Downloads: 58

Metrics Information