Classification of diabetic retinopathy grades using CNN feature extraction to segment the lesion
DOI:
https://doi.org/10.22399/ijcesen.649Keywords:
Diabetic retinopathy, CNN feature extraction, Classification, Microvascular, ReLUAbstract
Diabetes's microvascular aftereffect, diabetic retinopathy (DR), is the primary cause of eyesight loss in the globe. In order to prevent vision impairment and to intervene promptly, early detection and precise classification of DR severity are essential. Using standard methods for diagnosing DR requires ophthalmologists to grade cases by hand, a process that can be laborious, subjective, and subject to observer error. In supervised learning task of classification, data instances are classified into predefined classes based on features. The relation between the traits and the classes can be found from the labelled data. After the training is completed, the classes of the unseen data. The frequent reason found for the loss of vision in diabetic retinopathy (DR) is found to be diabetes. Visual damage can be prevented by identifying the degree of DR at right time. For the grading of the DR, deep learning techniques are found to be very effective with maximum possible accuracy. The proposed model is useful in accurately classifying the DR images using the feature extraction with lesion segmentation, by implementing the patterns in the DR images. ReLU activation function is used in the proposed model. CNN feature extraction is used for the important feature extraction by applying the Convolution layers, and edges, textures, and forms are identified. As the model proceeds layer by layer, complicated patterns in the photos can be learned by the model, and can be analysed better. The features of the photos were extracted and found useful in segmentation and classification. ReLU is helpful in improving the convergence and also found useful in learning the patterns. Among the other activation functions, ReLU has higher computational efficiency and therefore is used in the model, which suits well for the DR application. A strong framework is proposed for the classification of the DR grade, for the lesion segmentation and CNN feature extraction. DR categorization using the proposed model is evaluated by data visualization of the important calculated metrics and found to be very effective.
References
Zhou Yi, et al. (2020). A benchmark for studying diabetic retinopathy: segmentation, grading, and transferability. IEEE Transactions on Medical Imaging 40(3);818-828. doi: 10.1109/TMI.2020.3037771.
Shaukat, Natasha, et al. (2022). Three-Dimensional Semantic Segmentation of Diabetic Retinopathy Lesions and Grading Using Transfer Learning. Journal of Personalized Medicine 12(9); 1454. https://doi.org/10.3390/jpm12091454
Abdelmaksoud, Eman, et al. (2021). Automatic diabetic retinopathy grading system based on detecting multiple retinal lesions. IEEE Access 9; 15939-15960. doi: 10.1109/ACCESS.2021.3052870
Sau, Paresh Chandra, and Atul Bansal. (2022) A novel diabetic retinopathy grading using modified deep neural network with segmentation of blood vessels and retinal abnormalities. Multimedia Tools and Applications 81;39605–39633. https://doi.org/10.1007/s11042-022-13056-y
Kaur, Jaspreet, and Prabhpreet Kaur. (2022) UNIConv: An enhanced U‐Net based InceptionV3 convolutional model for DR semantic segmentation in retinal fundus images. Concurrency and Computation: Practice and Experience 34(21); e7138. https://doi.org/10.1002/cpe.7138
Wang, Hualin, et al. (2022) Anomaly segmentation in retinal images with poisson-blending data augmentation. Medical Image Analysis 81; 102534. DOI: 10.1016/j.media.2022.102534
Amin, Javaria, Muhammad Almas Anjum, and Muhammad Malik. (2022). Fused information of DeepLabv3+ and transfer learning model for semantic segmentation and rich features selection using equilibrium optimizer (EO) for classification of NPDR lesions. Knowledge-Based Systems 249;108881. https://doi.org/10.1016/j.knosys.2022.108881
Huang, Shiqi, et al. (2022) RTNet: Relation transformer network for diabetic retinopathy multi-lesion segmentation. IEEE Transactions on Medical Imaging. 41(6):1596-1607. doi: 10.1109/TMI.2022.3143833.
He, Yunlong, et al. (2019) Segmenting diabetic retinopathy lesions in multispectral images using low-dimensional spatial-spectral matrix representation. IEEE journal of biomedical and health informatics 24(2);493-502. doi: 10.1109/JBHI.2019.2912668.
Li, Tao, et al. (2019). Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening. Information Sciences 501;511-522. https://doi.org/10.1016/j.ins.2019.06.011
Playout, Clément, Renaud Duval, and Farida Cheriet. (2019). A novel weakly supervised multitask architecture for retinal lesions segmentation on fundus images. IEEE transactions on medical imaging 38(10);2434-2444. doi: 10.1109/TMI.2019.2906319.
Sarhan, Mhd Hasan, et al. (2020). Microaneurysms segmentation and diabetic retinopathy detection by learning discriminative representations. IET Image Processing 14(17);4571-4578. https://doi.org/10.1049/iet-ipr.2019.0804
Kreitner, Linus, et al. (2022). Automated analysis of diabetic retinopathy using vessel segmentation maps as inductive bias. arXiv preprint arXiv:2210.16053.
Guo, Yanfei, and Yanjun Peng. (2022) CARNet: Cascade attentive RefineNet for multi-lesion segmentation of diabetic retinopathy images. Complex & Intelligent Systems 8(2);1681-1701. https://doi.org/10.1007/s40747-021-00630-4
Wang, Xiang-Ning, et al. (2022). Automatic grading system for diabetic retinopathy diagnosis using deep learning artificial intelligence software. Current Eye Research 45(12);1550-1555. doi: 10.1080/02713683.2020.1764975
Abbood, Saif Hameed, et al. (2022) DR-LL Gan: Diabetic Retinopathy Lesions Synthesis using Generative Adversarial Network. International Journal of Online & Biomedical Engineering 18(3);151-163. DOI: 10.3991/ijoe.v18i03.28005
Xia, Haiying, et al. (2021). A multi-scale segmentation-to-classification network for tiny microaneurysm detection in fundus images." Knowledge-Based Systems 226;107140. https://doi.org/10.1016/j.knosys.2021.107140
Zhou, Yi, et al. (2020). DR-GAN: conditional generative adversarial network for fine-grained lesion synthesis on diabetic retinopathy images." IEEE Journal of Biomedical and Health Informatics 26(1):56-66. doi: 10.1109/JBHI.2020.3045475.
Xie, Yutong, et al. (2020). SESV: Accurate medical image segmentation by predicting and correcting errors. IEEE Transactions on Medical Imaging 40(1);286-296. doi: 10.1109/TMI.2020.3025308.
Guo, Yanfei, and Yanjun Peng. (2022) Multiple lesion segmentation in diabetic retinopathy with dual-input attentive RefineNet. Applied Intelligence: 52;14440–14464. https://doi.org/10.1007/s10489-022-03204-0
Bhardwaj, Charu, Shruti Jain, and Meenakshi Sood. (2021) Hierarchical severity grade classification of non-proliferative diabetic retinopathy. Journal of Ambient Intelligence and Humanized Computing 12(2);2649-2670. https://doi.org/10.1007/s12652-020-02426-9
Z. Gao, J. Li, J. Guo, Y. Chen, Z. Yi and J. Zhong, (2019). Diagnosis of Diabetic Retinopathy Using Deep Neural Networks, IEEE Access, 7;3360-3370, doi: 10.1109/ACCESS.2018.2888639.
Jabbar MK, Yan J, Xu H, Ur Rehman Z, Jabbar A. (2022). Transfer Learning-Based Model for Diabetic Retinopathy Diagnosis Using Retinal Images. Brain Sci. 22;12(5):535. doi: 10.3390/brainsci12050535.
M. M. Farag, M. Fouad and A. T. Abdel-Hamid, (2022). Automatic Severity Classification of Diabetic Retinopathy Based on DenseNet and Convolutional Block Attention Module, IEEE Access, 10;38299-38308, doi: 10.1109/ACCESS.2022.3165193.
S. Cahoon, M. Shaban, A. Switala, A. Mahmoud and A. El-Baz, (2022). Diabetic Retinopathy Screening Using a Two-Stage Deep Convolutional Neural Network Trained on An Extremely Un-Balanced Dataset, SoutheastCon 2022;250-254, doi: 10.1109/SoutheastCon48659.2022.9764079.
Lee, Chu-Hui, and Yi-Hsuan Ke. (2021). Fundus images classification for Diabetic Retinopathy using Deep Learning. 2021 The 13th International Conference on Computer Modeling and Simulation.
Majumder, Sharmin, and Nasser Kehtarnavaz. (2021). Multitasking deep learning model for detection of five stages of diabetic retinopathy. IEEE Access 9;123220-123230.
S. Majumder and N. Kehtarnavaz, (2021). Multitasking Deep Learning Model for Detection of Five Stages of Diabetic Retinopathy, IEEE Access, 9;123220-123230, doi: 10.1109/ACCESS.2021.3109240.
M. Tavakoli, A. Mehdizadeh, A. Aghayan, R. P. Shahri, T. Ellis and J. Dehmeshki, (2021). Automated Microaneurysms Detection in Retinal Images Using Radon Transform and Supervised Learning: Application to Mass Screening of Diabetic Retinopathy, IEEE Access, 9;67302-67314, doi: 10.1109/ACCESS.2021.3074458.
F. Saeed, M. Hussain and H. A. Aboalsamh, (2021). Automatic Diabetic Retinopathy Diagnosis Using Adaptive Fine-Tuned Convolutional Neural Network, IEEE Access, 9;41344-41359, doi: 10.1109/ACCESS.2021.3065273.
Ma, Y., Luo, Y., and Yang, Z. (2020). PCFNet: Deep neural network with predefined convolutional filters. Neurocomputing. 382, 32-39. https://doi.org/10.1016/j.neucom.2019.11.075
Jang, S. -I., JA Girard, M., and Thiery, A. H. (2021). Explainable diabetic retinopathy classification based on neural-symbolic learning. Proceedings of the 15th International Workshop on Neural-Symbolic Learning and Reasoning. CEUR Workshop Proceedings. 2986, 104-114.
Liu, X., Li, S., Ge, Y., Ye, P., You, J., & Lu, J. (2021). Recursively conditional gaussian for ordinal unsupervised domain adaptation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 764-773).
Kori, Avinash, et al. (2018). Ensemble of Convolutional Neural Networks for Automatic Grading of Diabetic Retinopathy and Macular Edema" arXiv:1809.04228.
Mohammed Safwan, Sai SakethChennamsetty, Avinash Kori, Varghese Alex, Ganapathy Krishnamurthi, (2018). Classification of Breast Cancer and Grading of Diabetic Retinopathy & Macular Edema using Ensemble of Pre-trained Convolutional Neural Networks, MIDL Conference Paper110 AnonReviewer2.
He, J., Shen, L., Ai, X., and Li, X. (2019). Diabetic retinopathy grade and macular edema risk classification using convolutional neural networks. 2019 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS). IEEE, 2019, 463-466. doi: 10.1109/ICPICS47731.2019.8942426.
Harangi, B., Toth, J., Baran, A., and Hajdu, A. (2019). Automatic screening of fundus images using a combination of convolutional neural network and hand-crafted features. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, 2699-2702, doi: 10.1109/EMBC.2019.8857073.
Li, X., et al. (2019). CANet: cross-disease attention network for joint diabetic retinopathy and diabetic macular edema grading. IEEE Transactions on Medical Imaging. 39(5);1483-1493.
Tu, Z., et al. (2020). SUNet: A lesion regularized model for simultaneous diabetic retinopathy and diabetic macular edema grading. 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). IEEE, 1378-1382. doi: 10.1109/ISBI45749.2020.9098673.
Reddy, V. P. C., and Gurrala, K. K. (2022). Joint DR-DME classification using deep learning-CNN based modified grey-wolf optimizer with variable weights. Biomedical Signal Processing and Control. 73;103439. https://doi.org/10.1016/j.bspc.2021.103439
Chaudhary, P. K., and Pachori, R. B. (2022). Automatic diagnosis of different grades of diabetic retinopathy and diabetic macular edema using 2D-FBSE-FAWT. IEEE Transactions on Instrumentation and Measurement. doi: 10.1109/TIM.2022.3140437.
Ma, Y., and Yang, Z. (2021). Multi-instance learning by utilizing structural relationship among instances. arXiv preprint arXiv:2102.01889.
Nazir, T., et al. (2021) Detection of diabetic eye disease from retinal images using a deep learning based CenterNet model. Sensors 21(16);5283. doi: 10.3390/s21165283.
Priti Parag Gaikwad, & Mithra Venkatesan. (2024). KWHO-CNN: A Hybrid Metaheuristic Algorithm Based Optimzed Attention-Driven CNN for Automatic Clinical Depression Recognition . International Journal of Computational and Experimental Science and Engineering, 10(3);491-506. https://doi.org/10.22399/ijcesen.359
PATHAPATI, S., N. J. NALINI, & Mahesh GADIRAJU. (2024). Comparative Evaluation of EEG signals for Mild Cognitive Impairment using Scalograms and Spectrograms with Deep Learning Models. International Journal of Computational and Experimental Science and Engineering, 10(4);859-866. https://doi.org/10.22399/ijcesen.534
Rama Lakshmi BOYAPATI, & Radhika YALAVARTHI. (2024). RESNET-53 for Extraction of Alzheimer’s Features Using Enhanced Learning Models. International Journal of Computational and Experimental Science and Engineering, 10(4);879-889. https://doi.org/10.22399/ijcesen.519
J Jeysudha, K. Deiwakumari, C.A. Arun, R. Pushpavalli, Ponmurugan Panneer Selvam, & S.D. Govardhan. (2024). Hybrid Computational Intelligence Models for Robust Pattern Recognition and Data Analysis. International Journal of Computational and Experimental Science and Engineering, 10(4);1032-1040. https://doi.org/10.22399/ijcesen.624
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 International Journal of Computational and Experimental Science and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
