Ophthalmic Diseases Classification Based on YOLOv8

Ahmed Tuama Khalaf, Salwa Khalid Abdulateef

Abstract


With the rising prevalence of retinal diseases, identifying eye diseases at an early stage is crucial for effective treatment and prevention of irreversible blindness. But Ophthalmologists face challenges in detecting subtle symptoms that may indicate the presence of a disease before it progresses to an advanced stage Among these challenges, eye diseases can present with a wide range of symptoms, and some conditions may share similar signs. To solve these difficulties, in the research proposed YOLOV8(You Only Look Once) Lightweight Self-Attention model to classify seven different retinal diseases. In this regard, the dataset that have been used in this study contains 5787 images from three different sources (Roboflow, Kaggle and Medical Clinics) were included in the seven classes of Glaucoma, Age-related Macular Degeneration (AMD), Cataract, Diabetic retinopathy (DR), and Retinal Vein Occlusion, which comprises of Branch Retinal Vein Occlusion (BRVO) and Central Retinal Occlusion (CRVO) and normal.  As a results, the model has proven excellent performance in its classification ability. Boasting an average classification accuracy of 94% across the seven disease with precsition 96.2%, recall 96.6%and f1 score was 96.3% At the time of training it was 0.6 Houres(H). When compaired with Resnet50, VGG16 results underscore the model’s superior performance in precision and computational efficiency compared. The algorithm's evaluation reveals its superiority when compared to earlier pertinent research, making it a trustworthy method for classifying retinal illnesses.

Keywords


Ophthalmic Disease; Deep Learning; Fundus Image; Yolov8; Resnet50; Vgg16.

Full Text:

PDF

References


World Health Organization, Blindness and vision impairment. World Health Organization, 2023, https://www.who.int/news-room/fact-sheets/detail/blindness-and-visual-impairment.

X. Luo, J. Li, M. Chen, X. Yang, and X. Li, "Ophthalmic disease detection via deep learning with a novel mixture loss function," IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 9, pp. 3332-3339, 2021, doi: 10.1109/JBHI.2021.3083605.

A.K. Bitto, M. H. Bijoy, M. S. Arman, I. Mahmud, A. Das, and J. Majumder, "Sentiment analysis from Bangladeshi food delivery startup based on user reviews using machine learning and deep learning," Bulletin of Electrical Engineering and Informatics, vol. 12, no. 4, pp. 2282-2291, 2023, doi: 10.11591/eei.v12i4.4135.

E. Valeriani et al., "Antithrombotic treatment for retinal vein occlusion: a systematic review and meta-analysis," Journal of Thrombosis and Haemostasis, vol. 21, no. 2, pp. 284-293, 2023, doi: 10.1016/j.jtha.2022.10.003.

J. H. Wu, T. Nishida, R. N. Weinreb, and J. W. Lin, "Performances of machine learning in detecting glaucoma using fundus and retinal optical coherence tomography images: a meta-analysis," American Journal of Ophthalmology, vol. 237, pp. 1-12, 2022, doi: 10.1016/j.ajo.2021.12.008.

A. Aslam, S. Farhan, M. A. Khaliq, F. Anjum, A. Afzaal, and F. Kanwal, "Convolutional Neural Network-Based Classification of Multiple Retinal Diseases Using Fundus Images," Intelligent Automation & Soft Computing, vol. 36, no. 3, pp. 2607-2622, 2023, doi: 10.3390/bioengineering10010025.

A. Misra, H. Gopalan, R. Jayawardena, A. P. Hills, M. Soares, A. A. Reza‐Albarrán, and K. L. Ramaiya, "Diabetes in developing countries," Journal of Diabetes, vol. 11, no.7, pp. 522-539, 2019, doi: 10.1111/1753-0407.12913.

R. Pradeepa and V. Mohan, "Epidemiology of type 2 diabetes in India," Indian journal of ophthalmology, vol. 69, no. 11, 2932, 2021, doi: 10.4103/ijo.IJO_1627_21.

Y. Liang, M. Li, Y. Yang, L. Qiao, H. Xu, and B. Guo, "pH/glucose dual responsive metformin release hydrogel dressings with adhesion and self-healing via dual-dynamic bonding for athletic diabetic foot wound healing," ACS nano, vol. 16, no. 2, pp. 3194-3207, 2022, doi: 10.1021/acsnano.1c11040.

J. C. Chan et al., "The lancet Commission on diabetes: using data to transform diabetes care and patient lives," The Lancet, vol. 396, no. 10267, pp. 2019-2082, 2020, doi: 10.1016/S0140-6736(20)32374-6.

X. Lin et al., "Global, regional, and national burden and trend of diabetes in 195 countries and territories: an analysis from 1990 to 2025," Scientific reports, vol. 10, no. 1, p. 14790, 2020, doi: 10.1038/s41598-020-71908-9.

C. K. Sen, "Human wound and its burden: updated 2020 compendium of estimates," Advances in wound care, vol. 10, no. 5, pp. 281-292, 2021, doi: 10.1089/wound.2021.0026.

L. Wang et al., "Prevalence and treatment of diabetes in China, 2013-2018," Jama, vol. 326, no. 24, pp. 2498-2506, 2021, doi: 10.1001/jama.2021.22208.

Y. Zheng, S. H. Ley, and F. B. Hu, "Global aetiology and epidemiology of type 2 diabetes mellitus and its complications," Nature reviews endocrinology, vol. 14, no. 2, pp. 88-98, 2018, doi: 10.1038/nrendo.2017.151.

S. Dutta, B. Manideep, S. M. Basha, R. D. Caytiles, and N. Iyengar, "Classification of diabetic retinopathy images by using deep learning models," International Journal of Grid and Distributed Computing, vol. 11, no. 1, pp. 89-106, 2018, doi: 10.14257/ijgdc.2018.11.1.09.

A. D. Bhatwadekar, A. Shughoury, A. Belamkar, and T. A. Ciulla, "Genetics of diabetic retinopathy, a leading cause of irreversible blindness in the industrialized world," Genes, vol. 12, no. 8, p. 1200, 2021, doi: 10.3390/genes12081200.

I. S. Forrest et al., "Genome-wide polygenic risk score for retinopathy of type 2 diabetes," Human Molecular Genetics, vol. 30, no. 10, pp. 952-960, 2021, doi: 10.1093/hmg/ddab067.

A. Qassim, E. Souzeau, G. Hollitt, M. M. Hassall, O. M. Siggs, and J. E. Craig, "Risk stratification and clinical utility of polygenic risk scores in ophthalmology," Translational Vision Science & Technology, vol. 10, no. 6, pp. 14-14, 2021, doi: 10.1167/tvst.10.6.14.

L. Nicholson, S. J. Talks, W. Amoaku, K. Talks, and S. Sivaprasad, "Retinal vein occlusion (RVO) guideline: executive summary," Eye, vol. 36, no. 5, pp. 909-912, 2022, doi: 10.1038/s41433-022-02007-4.

E. Valeriani et al., "Antithrombotic treatment for retinal vein occlusion: a systematic review and meta-analysis," Journal of Thrombosis and Haemostasis, vol. 21, no. 2, pp. 284-293, 2023, doi: 10.1016/j.jtha.2022.10.003.

D. Nagasato et al., "Automated detection of a nonperfusion area caused by retinal vein occlusion in optical coherence tomography angiography images using deep learning," PloS one, vol. 14, no. 11, p. e0223965, 2019, doi: 10.1371/journal.pone.0223965.

R. Aggarwal et al., "Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis," NPJ digital medicine, vol. 4, no. 1, 2021, doi: 10.1038/s41746-021-00438-z.

P. K. Mall et al., "A comprehensive review of deep neural networks for medical image processing: Recent developments and future opportunities," Healthcare Analytics, vol. 4, no. 1, p. 100216, 2023, doi: 10.1016/j.health.2023.100216.

Q. Chen et al., "Artificial intelligence can assist with diagnosing retinal vein occlusion," International Journal of Ophthalmology, vol. 14, no. 12, pp. 1895–1902, 2021, doi: 10.18240/ijo.2021.12.13.

D. Yang et al., "Deep learning in optical coherence tomography angiography: Current progress, challenges, and future directions," Diagnostics, vol. 13, no. 2, 2023, doi: 10.3390/diagnostics13020326.

A. L. Rothman, A. S. Thomas, K. Khan, and S. Fekrat, "Central retinal vein occlusion in young individuals: a comparison of risk factors and clinical outcomes," Retina, vol. 39, no. 10, pp. 1917-1924, 2019, doi: 10.1097/IAE.0000000000002278.

S. S. Hayreh, "Photocoagulation for retinal vein occlusion," Progress in retinal and eye research, vol. 85, p. 100964, 2021, doi: 10.1016/j.preteyeres.2021.100964.

X. T. Zhang et al., "Clinical features of central retinal vein occlusion in young patients," Ophthalmology and Therapy, vol. 11, no. 4, pp. 1409-1422, 2022, doi: 10.1007/s40123-022-00534-7.

Y. Tang, Y. Cheng, S. Wang, Y. Wang, P. Liu, and H. Wu, "The development of risk factors and cytokines in retinal vein occlusion," Frontiers in Medicine, vol. 9, p. 910600, 2022, doi: 10.3389/fmed.2022.910600.

D. Nagasato et al., "Deep neural network-based method for detecting central retinal vein occlusion using ultrawide-field fundus ophthalmoscopy," Journal of ophthalmology, vol. 1, 2018.doi: 10.1155/2018/1875431.

J. P. Li et al., "Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective," Progress in retinal and eye research, vol. 82, p. 100900, 2021, doi: 10.1016/j.preteyeres.2020.100900.

L. P. Cen et al., "Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks," Nature communications, vol. 12, no, 1, p. 4828, 2021, doi: 10.1038/s41467-021-25138-w.

J. Xu, K. Xue, and K. Zhang, "Current status and future trends of clinical diagnoses via image-based deep learning," Theranostics, vol. 9, no. 25, p. 7556, 2019, doi: 10.7150/thno.38065.

M. M. Butt, D. A. Iskandar, S. E. Abdelhamid, G. Latif, and R. Alghazo, "Diabetic retinopathy detection from fundus images of the eye using hybrid deep learning features," Diagnostics, vol. 12, no. 7, p. 1607, 2022, doi: 10.3390/diagnostics12071607.

S. Jabbehdari, G. Yazdanpanah, L. B. Cantor, and A. R. Hajrasouliha, "A narrative review on the association of high intraocular pressure and glaucoma in patients with retinal vein occlusion," Annals of Translational Medicine, vol. 10, no. 19, p. 1072, 2022, doi: 10.21037/atm-22-2730.

Y. B. Kim, C. H. Lee, Y. K. Shin, S. E. Kyung, and Y. S. Seo, "Early retinal hemorrhage absorption rate and long term clinical outcomes in branch retinal vein occlusion," Journal of the Korean Ophthalmological Society, vol. 62, no. 4, pp. 496-506, 2021.

S. C. Akdemir and B. O. Gunay, "Comparison of anti-VEGF results between non-ischemic branch retinal vein occlusion and ischemic branch retinal vein occlusion with early sector panretinal photocoagulation," Journal Français 'Ophtalmologie, vol. 45, no. 9, pp. 1042-1047, 2022, doi: 10.1016/j.jfo.2022.03.014.

J. Miao et al., "Deep learning models for segmenting non-perfusion area of color fundus photographs in patients with branch retinal vein occlusion," Frontiers in Medicine, vol. 9, p. 794045, 2022, doi: 10.3389/fmed.2022.794045.

Y. Ji, Y. Ji, Y. Liu, Y. Zhao, and L. Zhang, "Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images," Frontiers in Cell and Developmental Biology, vol. 11, p. 1168327, 2023, doi: 10.3389/fcell.2023.1168327.

I. Weni, P. E. Utomo, B. F. Hutabarat, and M. Alfalah, "Detection of cataract based on image features using convolutional neural networks," Indonesian Journal of Computing and Cybernetics Systems, vol. 15, no. 1, pp. 75-86, 2021, doi: 10.22146/ijccs.61882.

S. H. Heruye et al., "Opere, Current trends in the pharmacotherapy of cataracts," Pharmaceuticals, vol. 13, no. 1, 2020, doi: 10.3390/ph13010015.

Y. Elloumi, "Cataract grading method based on deep convolutional neural networks and stacking ensemble learning," International Journal of Imaging Systems and Technology, vol. 32, no. 3, pp. 798-814, 2022, doi: 10.1002/ima.22722.

J. Wang et al., "A transformer-based knowledge distillation network for cortical cataract grading," IEEE Transactions on Medical Imaging, 2023, doi: 10.1109/TMI.2023.3327274.

S. Serte and A. Serener, "A generalized deep learning model for glaucoma detection," in 2019 3rd International symposium on multidisciplinary studies and innovative technologies (ISMSIT), pp. 1-5, 2019, doi: 10.1109/ISMSIT.2019.8932753.

S. Sotoudeh-Paima, A. Jodeiri, F. Hajizadeh, and H. Soltanian-Zadeh, "Multi-scale convolutional neural network for automated AMD classification using retinal OCT images," Computers in biology and medicine, vol. 144, pp. 105368, 2022, doi: 10.1016/j.compbiomed.2022.105368.

A. Thomas, P. Harikrishnan, A. K. Krishna, P. Palanisamy, and V. P. Gopi, "A novel multiscale convolutional neural network based age-related macular degeneration detection using OCT images," Biomedical Signal Processing and Control, vol. 67, p. 102538, 2021, doi: 10.1016/j.cmpb.2021.106294.

M. H. Najim, S. K. Abdulateef, and A. H. Alasadi," Early detection of tomato leaf diseases based on deep learning techniques," International Journal of Artificial Intelligence (IJ-AI), vol. 13, no. 1, pp. 509-515, 2023, doi: 10.11591/ijai.v13.i1.pp509-515.

S. Sengupta et al., "A review of deep learning with special emphasis on architectures, applications and recent trends," Knowledge-Based Systems, vol. 194, p. 105596, 2020, doi: 10.1016/j.knosys.2020.105596.

R. Sarki, K. Ahmed, H. Wang, Y. Zhang, and K. Wang, "Convolutional neural network for multi-class classification of diabetic eye disease," EAI Endorsed Transactions on Scalable Information Systems, vol. 9, no. 4, 2021, doi: 10.4108/eai.16-12-2021.172436.

A. Ali et al., "Machine learning based automated segmentation and hybrid feature analysis for diabetic retinopathy classification using fundus image," Entropy, vol. 22, no. 5, p. 567, 2020, doi: 10.3390/e22050567.

R. Narlan and E. P. Widiyanto, "Automated pavement defect detection using YOLOv8 object detection algorithm," Prosiding KRTJ HPJI, vol. 16, no. 1, pp. 1-13, 2023, doi: 10.58674/phpji.v16i1.388.

G. G. Casas, Z. H. Ismail, M. M. Limeira, A. A. da Silva, and H. G. Leite, "Automatic detection and counting of stacked eucalypt timber using the YOLOv8 model," Forests, vol. 14, no. 12, p. 2369, 2023, doi: 10.3390/f14122369.

F. Khan, N. Zafar, M. N. Tahir, M. Aqib, H. Waheed, and Z. A. Haroon, "Mobile-based system for maize plant leaf disease detection and classification using deep learning," Frontiers in Plant Science, vol. 14, p. 1079366, 2023, doi: 10.3389/fpls.2023.1079366.

A. Inui et al., "Detection of elbow OCD in the ultrasound image by artificial intelligence using YOLOv8," Applied Sciences, vol. 13, no.13, 2023, doi: 10.3390/app13137623.

M. Alruwaili et al., "Deep Learning-Based YOLO Models for the Detection of People With Disabilities," in IEEE Access, vol. 12, pp. 2543-2566, 2024, doi: 10.1109/ACCESS.2023.3347169.

G. Jocher, A. Chaurasia, and J. Qiu, "YOLO by Ultralytics," URL: https://github.com/ultralytics/ultralytics, 2023.

X. Li et al., "Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection," Advances in Neural Information Processing Systems, vol. 33, pp. 21002-21012, 2020.

X. Wang, H. Li, X. Yue, and L. Meng, "A comprehensive survey on object detection YOLO," Proceedings http://ceur-ws.org ISSN, vol. 1613, p. 0073, 2023.

S. Dash, J. Supraja, D. Vanshitha, C. Keerthi, and E. Dinesh, "Retinal Disease Prediction Using Deep Learning," Journal of Engineering Sciences, vol. 14, no. 7, 2023, doi: 10.3390/jimaging9040084.

E. Abitbol et al., "Deep learning-based classification of retinal vascular diseases using ultra-widefield colour fundus photographs," BMJ Open Ophthalmology, vol. 7, no. 1, p. e000924, 2022, doi: 10.1136/bmjophth-2021-000924.

W. Xu, Z. Yan, N. Chen, Y. Luo, Y. Ji, M. Wang, and Z. Zhang, "Development and application of an intelligent diagnosis system for retinal vein occlusion based on deep learning," Disease Markers, vol. 2022, 2022, doi: 10.1155/2022/4988256.

T. Babaqi, M. Jaradat, A. E. Yildirim, S. H. Al-Nimer, and D. Won, "Eye disease classification using deep learning techniques," arXiv preprint arXiv: 2307.10501, 2023, doi: 10.48550/arXiv.2307.10501.

C. Wan, R. Hua, K. Li, X. Hong, D. Fang, and W. Yang, "Automatic diagnosis of different types of retinal vein occlusion based on fundus images," International Journal of Intelligent Systems, vol. 2023, 2022, doi: 10.1155/2023/1587410.

S. K. Abdulateef, A. N. Ismael, M. D. Salman," Feature weighting for parkinson's identification using single hidden layer neural network," International Journal of Computing, vol. 22, no. 2, pp. 225-230, 2022, doi: 10.47839/ijc.22.2.3092.




DOI: https://doi.org/10.18196/jrc.v5i2.21208

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Ahmed Tuama Khalaf, Salwa Khalid Abdulateef

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

 

Journal of Robotics and Control (JRC)

P-ISSN: 2715-5056 || E-ISSN: 2715-5072
Organized by Peneliti Teknologi Teknik Indonesia
Published by Universitas Muhammadiyah Yogyakarta in collaboration with Peneliti Teknologi Teknik Indonesia, Indonesia and the Department of Electrical Engineering
Website: http://journal.umy.ac.id/index.php/jrc
Email: jrcofumy@gmail.com

Kuliah Teknik Elektro Terbaik