Diabetic Retinopathy Detection using the Genetic Algorithm and a Channel Attention Module on Hybrid VGG16 and EfficientNetB0
Received: 26 November 2024 | Revised: 13 December 2024 and 2 January 2025 | Accepted: 6 January 2025 | Online:
Corresponding author: Satti Mounika
Abstract
Diabetic Retinopathy (DR), a result of diabetes, requires early detection to reduce the impact of the disease on vision. This study introduces a new system whose architecture is based on a combination of VGG16 architecture with EfficientNetB0 as well as an added body structure, which is the Channel Attention Module (CAM), to strengthen the channel maps and thus achieve improved classification accuracy. For further efficiency and consistency, the system employs a genetic algorithm for image normalization. The system shows great potential for improving clinical decision making and patient examination results when used in the diagnosis of DR. The evaluation results confirm the reliability of the system and the feasibility of using it in daily practice to address the acute challenge of early detection of DR. The model is well trained with a test dataset of 2900 images and demonstrates high accuracy of 95%. This high accuracy clearly shows the high reliability of the proposed hybrid model which is also confirmed by the precision and recall values. The achieved precision is 0.96 for class 0 and 0.94 for class 1, and the achieved recall is 0.94 for class 0 and 0.97 for class 1.
Keywords:
diabetic retinopathy, hybrid model, VGG16, EfficientNetB0, Channel Attention Module, genetic algorithm, image alignmentDownloads
References
N. B. Thota and D. Umma Reddy, "Improving the Accuracy of Diabetic Retinopathy Severity Classification with Transfer Learning," in 2020 IEEE 63rd International Midwest Symposium on Circuits and Systems, Springfield, MA, USA, 2020, pp. 1003–1006.
R. Ghosh, K. Ghosh, and S. Maitra, "Automatic detection and classification of diabetic retinopathy stages using CNN," in 2017 4th International Conference on Signal Processing and Integrated Networks, Noida, India, 2017, pp. 550–554.
R. A. Welikala et al., "Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy," Computerized Medical Imaging and Graphics, vol. 43, pp. 64–77, Jul. 2015.
B. Tymchenko, P. Marchenko, and D. Spodarets, "Deep Learning Approach to Diabetic Retinopathy Detection." arXiv, Mar. 03, 2020.
Z. Khan et al., "Diabetic Retinopathy Detection Using VGG-NIN a Deep Learning Architecture," IEEE Access, vol. 9, pp. 61408–61416, 2021.
X. Luo et al., "A deep convolutional neural network for diabetic retinopathy detection via mining local and long-range dependence," CAAI Transactions on Intelligence Technology, vol. 9, no. 1, pp. 153–166, Feb. 2024.
G. Kalyani, B. Janakiramaiah, A. Karuna, and L. V. N. Prasad, "Diabetic retinopathy detection and classification using capsule networks," Complex & Intelligent Systems, vol. 9, no. 3, pp. 2651–2664, Jun. 2023.
D. Das, S. K. Biswas, and S. Bandyopadhyay, "Detection of Diabetic Retinopathy using Convolutional Neural Networks for Feature Extraction and Classification (DRFEC)," Multimedia Tools and Applications, vol. 82, no. 19, pp. 29943–30001, Aug. 2023.
S. Wan, Y. Liang, and Y. Zhang, "Deep convolutional neural networks for diabetic retinopathy detection by image classification," Computers & Electrical Engineering, vol. 72, pp. 274–282, Nov. 2018.
R. Pires, S. Avila, J. Wainer, E. Valle, M. D. Abramoff, and A. Rocha, "A data-driven approach to referable diabetic retinopathy detection," Artificial Intelligence in Medicine, vol. 96, pp. 93–106, May 2019.
E. V. Carrera, A. González, and R. Carrera, "Automated detection of diabetic retinopathy using SVM," in 2017 IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing, Cusco, Peru, 2017, pp. 1–4.
U. Ishtiaq, S. Abdul Kareem, E. R. M. F. Abdullah, G. Mujtaba, R. Jahangir, and H. Y. Ghafoor, "Diabetic retinopathy detection through artificial intelligent techniques: a review and open issues," Multimedia Tools and Applications, vol. 79, no. 21, pp. 15209–15252, Jun. 2020.
Z. Wang and J. Yang, "Diabetic Retinopathy Detection via Deep Convolutional Networks for Discriminative Localization and Visual Explanation." arXiv, Dec. 02, 2019.
S. Patel, "Diabetic Retinopathy Detection and Classification using Pre-trained Convolutional Neural Networks," International Journal on Emerging Technologies, vol. 11, no. 3, pp. 1082–1087, 2020.
C. Lahmar and A. Idri, "On the value of deep learning for diagnosing diabetic retinopathy," Health and Technology, vol. 12, no. 1, pp. 89–105, Jan. 2022.
D. Doshi, A. Shenoy, D. Sidhpura, and P. Gharpure, "Diabetic retinopathy detection using deep convolutional neural networks," in 2016 International Conference on Computing, Analytics and Security Trends, Pune, India, 2016, pp. 261–266.
G. García, J. Gallardo, A. Mauricio, J. López, and C. Del Carpio, "Detection of Diabetic Retinopathy Based on a Convolutional Neural Network Using Retinal Fundus Images," in 26th International Conference on Artificial Neural Networks, Alghero, Italy, 2017, pp. 635–642.
S. Anwar, S. R. Soomro, S. K. Baloch, A. A. Patoli, and A. R. Kolachi, "Performance Analysis of Deep Transfer Learning Models for the Automated Detection of Cotton Plant Diseases," Engineering, Technology & Applied Science Research, vol. 13, no. 5, pp. 11561–11567, Oct. 2023.
H. M. Zayani et al., "Deep Learning for Tomato Disease Detection with YOLOv8," Engineering, Technology & Applied Science Research, vol. 14, no. 2, pp. 13584–13591, Apr. 2024.
"Diagnosis of Diabetic Retinopathy." Kaggle, [Online]. Available: https://www.kaggle.com/datasets/pkdarabi/diagnosis-of-diabetic-retinopathy.
W. L. Alyoubi, M. F. Abulkhair, and W. M. Shalash, "Diabetic Retinopathy Fundus Image Classification and Lesions Localization System Using Deep Learning," Sensors, vol. 21, no. 11, Jun. 2021, Art. no. 3704.
M. M. Islam, H.-C. Yang, T. N. Poly, W.-S. Jian, and Y.-C. (Jack) Li, "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, Jul. 2020, Art. no. 105320.
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