Deep Learning Empowered Diabetic Retinopathy Detection and Classification using Retinal Fundus Images
Article Sidebar
Main Article Content
Abstract
Diabetic Retinopathy (DR) is a commonly occurring disease among diabetic patients that affects retina lesions and vision. Since DR is irreversible, an earlier diagnosis of DR can considerably decrease the risk of vision loss. Manual detection and classification of DR from retinal fundus images is time-consuming, expensive, and prone to errors, contrasting to CAD models. In recent times, DL models have become a familiar topic in several applications, particularly medical image classification. With this motivation, this paper presents new deep learning-empowered diabetic retinopathy detection and classification (DL-DRDC) model. The DL-DRDC technique aims to recognize and categorize different grades of DR using retinal fundus images. The proposed model involves the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique as a pre-processing stage, which is used to enhance the contrast of the fundus images and improve the low contrast of medical images. Besides, the CLAHE is applied to the L channel of the retina images that have higher contrast. In addition, a deep learning-based Efficient Net-based feature extractor is used to generate feature vectors from pre-processed images. Moreover, a deep neural network (DNN) is used as a classifier model to allocate proper DR stages. An extensive set of experimental analyses takes place using a benchmark MESSIDOR dataset and the results are examined interms of different evaluation parameters. The simulation values highlighted the better DR diagnostic efficiency of the DL-DRDC technique over the recent techniques.
Article Details
References
Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM 2017;60(May (6)):84–90.
Hajeb Mohammad Alipour S, Rabbani H, Akhlaghi MR. Diabetic retinopathy grading by digital curvelet transform. Comput Math Methods Med 2012;2012:1–11.
Alyoubi, W.L., Shalash, W.M. and Abulkhair, M.F., 2020. Diabetic retinopathy detection through deep learning techniques: A review. Informatics in Medicine Unlocked, 20, p.100377.
Guo Y, Liu Y, Oerlemans A, Lao S, Wu S, Lew MS. Deep learning for visual understanding: a review. Neurocomputing 2016;187:27–48.
R. Kamalraj, M. Ranjith Kumar, V. Chandra ShekharRao, RohitAnand, Harinder Singh, "Interpretable filter based convolutional neural network (IF-CNN) for glucose prediction and classification using PD-SS algorithm", Measurement, Vol.183,2021, https://doi.org/10.1016/j.measurement.2021.109804.
Satpathy S, DebbarmaSwapana, SenguptaAditya S.C, BhattacaryyaBidyut K.D, "Design a FPGA, fuzzy based, insolent method for prediction of multi-diseases in rural area", Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 7039-7046, 2019. doi: 10.3233/JIFS-181577.
Kuna, Sri Laxmi and Prasad, Dr. A. V. Krishna, Deep Learning Models for Classification of Diabetic Retinopathy Color Fundus Images (September 14, 2022). Available at SSRN: https://ssrn.com/abstract=4218649 or http://dx.doi.org/10.2139/ssrn.4218649
Jayasankar, U., Thirumal, V. and Ponnurangam, D., 2018. A New Objective Image Quality Assessment Metric: For Color and Grayscale Images. 3D Research, 9(3), pp.1-16.
Paulraj, D 2020, ‘A gradient boosted decision tree-based sentiment classification of twitter data’, International Journal of Wavelets, Multiresolution and Information Processing, vol. 18, no. 4, pp. 205027 1-21. DOI: https://doi. org/10.1142/ S0219691320500277
SambitSatpathy, Sanchali Das, SwapanDebbarma, "A new healthcare diagnosis system using an IoT-based fuzzy classifier with FPGA", Journal of Supercomputing vol. 76, no. 8, pp. 5849–5861, 2020. doi: 10.1007/s11227-019-03013-2.
dos Santos, J.C.M., Carrijo, G.A., dos Santos Cardoso, C.D.F., Ferreira, J.C., Sousa, P.M. and Patrocínio, A.C., 2020. Fundus image quality enhancement for blood vessel detection via a neural network using CLAHE and Wiener filter. Research on Biomedical Engineering, pp.1-13.
Si, L., Xiong, X., Wang, Z. and Tan, C., 2020. A deep convolutional neural network model for intelligent discrimination between coal and rocks in coal mining face. mathematical Problems in Engineering, 2020.
Ben Jabra, M., Koubaa, A., Benjdira, B., Ammar, A. and Hamam, H., 2021. COVID-19 Diagnosis in Chest X-rays Using Deep Learning and Majority Voting. Applied Sciences, 11(6), p.2884.
Ramesh, S. and Vydeki, D., 2020. Recognition and classification of paddy leaf diseases using Optimized Deep Neural network with Jaya algorithm. Information processing in agriculture, 7(2), pp.249-260.
Haider, A., Arsalan, M., Lee, M.B., Owais, M., Mahmood, T., Sultan, H. and Park, K.R., 2022. Artificial Intelligence-based computer-aided diagnosis of glaucoma using retinal fundus images. Expert Systems with Applications, 207, p.117968.
Hemalakshmi, G.R., Santhi, D., Mani, V.R.S., Geetha, A. and Prakash, N.B., 2021. Classification of retinal fundus image using MS-DRLBP features and CNN-RBF classifier. Journal of Ambient Intelligence and Humanized Computing, 12(9), pp.8747-8762.
Kouroupis, M., Korfiatis, N. and Cornford, J., 2020. Artificial intelligence–assisted detection of diabetic retinopathy on digital fundus images: concepts and applications in the National Health Service. Innovation in Health Informatics, pp.261-278.
Dharmana, M.M. and Aiswarya, M.S., 2020, July. Pre-diagnosis of diabetic retinopathy using blob detection. In 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA) (pp. 98-101). IEEE.
Hassan, H.A., Yaakob, M., Ismail, S., Abd Rahman, J., Rusni, I.M., Zabidi, A., Yassin, I.M., Tahir, N.M. and Shafie, S.M., 2020, February. Detection of proliferative diabetic retinopathy in fundus images using convolution neural network. In IOP Conference Series: Materials Science and Engineering (Vol. 769, No. 1, p. 012029). IOP Publishing.
Freeda, J., Priyanka, K., Priyanka, S. and Sakthipriya, V.R., Detection of Disease from Human Fundus Images using Image Processing Approach.
Prem, S.S. and Umesh, A.C., 2020, October. Classification of exudates for diabetic retinopathy prediction using machine learning. In 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA) (pp. 357-362). IEEE.
Shanthini, A., Manogaran, G., Vadivu, G., Kottilingam, K., Nithyakani, P. and Fancy, C., 2021. Threshold segmentation based multi-layer analysis for detecting diabetic retinopathy using convolution neural network. Journal of Ambient Intelligence and Humanized Computing, pp.1-15.
Shoba, S.G. and Therese, A.B., 2020. Detection of glaucoma disease in fundus images based on morphological operation and finite element method. Biomedical Signal Processing and Control, 62, p.101986.
Vanakovarayan, S., Kumar, R.S., Chinnapparaj, S. and Kumar, S.S., 2021. Classification using fundus image of eye by CNN method and self-sufficient glaucoma detection using IOT. Materials Today: Proceedings, 37, pp.2812-2817.
S.Kannadhasan and R.Nagarajan, Performance Analysis of Circular Shape Microstrip Antenna for Wireless Communication System, AICTE Sponsored International E-Conference on Computing and Communication Systems for a Fourth Industrial Revolution (AICERA 2020), Amal Jyothi College of Engineering, Kerala, 14-16 December 2020, Published for IOP Conference Series: Materials Science and Engineering, Vol No: 1085, 2021, doi:10.1088/1757-899X/1085/1/012013
Michael David Abr`amoff, Yiyue Lou, Erginay Ali, Clarida Warren, Amelon Ryan, James C. Folk, MeindertNiemeijer, Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning, Invest. Opthalmol. Vis. Sci. 57 (13) (oct 2016) 5200, https://doi.org/10.1167/ iovs.16-19964. ISSN 1552–5783..
E. Perumal, M. Elhoseny and P. T. 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, 2021.
Sri Laxmi Kuna , Dr. A.V. Krishna Prasad , “An Efficient Meta-Heuristic-Feature Fusion Model using Deep Neuro-Fuzzy Classifier” International Journal of Advanced Computer Science and Applications, Vol. 13, No. 11, 2022
L. Liu, Y. Long, P. W. Fieguth, S. Lao and G. Zhao, "BRINT: Binary Rotation Invariant and Noise Tolerant Texture Classification," in IEEE Transactions on Image Processing, vol. 23, no. 7, pp. 3071-3084, July 2014, doi: 10.1109/TIP.2014.2325777.
Y. Zhou et al., "Speckle Noise Reduction for OCT Images Based on Image Style Transfer and Conditional GAN," in IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 1, pp. 139-150, Jan. 2022, doi: 10.1109/JBHI.2021.3074852.
Z. Yan, Y. Chen, M. Zhang and H. Lv, "Finite-Time Annular Domain Bounded Control of Itô-Type Stochastic Systems With Wiener and Poisson Random Disturbance," in IEEE Access, vol. 9, pp. 17284-17295, 2021, doi: 10.1109/ACCESS.2021.3053352.