ntvНаучно-технический вестник информационных технологий, механики и оптикиScientific and Technical Journal of Information Technologies, Mechanics and Optics2226-14942500-0373Университет ИТМО10.17586/2226-1494-2023-23-3-575-584ntv-29Research ArticleИСКУССТВЕННЫЙ ИНТЕЛЛЕКТ И КОГНИТИВНЫЕ ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИARTIFICIAL INTELLIGENCE AND COGNITIVE INFORMATION TECHNOLOGIESОбнаружение слепоты при диабетической ретинопатии с использованием алгоритма связанных компонентов на основе байесовского варианта в Keras и TensorFlowBlindness detection in diabetic retinopathy using Bayesian variant-based connected component algorithm in Keras and TensorFlowhttps://orcid.org/0000-0002-7446-0283Ананта БабуШ.Anantha BabuSh.

Ананта Бабу Шанмугавел — PhD, доцент

sc 57198087959

Хайдарабад, 500075, шт. Телангана 

Shanmugavel Anantha Babu — PhD, Associate Professor

sc 57198087959

Hyderabad, 500075, Telangana  

ananthmtech@gmail.comhttps://orcid.org/0000-0002-1631-8078МуралиС.MuraliS.

Мурали Субраманиан — PhD, доцент 

sc 55752705600 

Веллуру, 632014 

Subramanian Murali — PhD, Associate Professor 

sc 55752705600 

Vellore, 632014 

murali.s@vit.ac.inhttps://orcid.org/0000-0002-0207-0411ВиджаянЭ.VijayanE.

Виджаян Эллаппан — PhD, доцент 

sc 57216300541 

Веллуру, 632014 

Ellappan Vijayan — PhD, Associate Professor 

sc 57216300541 

Vellore, 632014 

evijayan@vit.ac.inhttps://orcid.org/0000-0002-7629-9606АнандМ.AnandM.

Ананд Махендран — PhD, доцент 

sc 56318694500 

Веллуру, 632014 

Mahendran Anand — PhD, Associate Professor 

sc 56318694500 

Vellore, 632014 

manand@vit.ac.inhttps://orcid.org/0000-0002-1532-5495РаманатанЛ.RamanathanL.

Раманатан Лакшманан — PhD, доцент 

sc 55808530000 

Веллуру, 632014 

Lakshmanan Ramanathan — PhD, Associate Professor 

sc 55808530000 

Vellore, 632014 

lramanathan@vit.ac.inУниверситет Конеру Лакшмайан (KL)ИндияKoneru Lakshmaiah Education FoundationIndiaТехнологический институт ВеллуруИндияVellore Institute of TechnologyIndia202311122024233575584Copyright © Ананта Бабу Ш., Мурали С., Виджаян Э., Ананд М., Раманатан Л., 20242024Ананта Бабу Ш., Мурали С., Виджаян Э., Ананд М., Раманатан Л.Anantha Babu S., Murali S., Vijayan E., Anand M., Ramanathan L.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://ntv.elpub.ru/jour/article/view/29

Нейродегенеративное заболевание глаз — глаукома, вызывается повышением внутриглазного давления сетчатки. Это вторая по значимости причина слепоты в мире. Отсутствие раннего диагноза приводит к полной слепоте. Актуальной проблемой является создание системы диагностики, которая может функционировать без большого количества оборудования, высококвалифицированного медицинского персонала и занимает мало времени. Предложенное в работе моделирование состоит из трех этапов: предварительная подготовка, тонкая настройка и логический вывод. Вероятностная пиксельная идентификация (байесовский вариант) позволяет прогнозировать тяжесть заболевания по наличию визуальных признаков, таких как аномалия кровеносных сосудов, наличие твердых экссудатов и ватообразных очагов. Рассмотрено сочетание машинного обучения, глубокого обучения и методов обработки изображений для оценки и идентификации диагностических изображений. Входное изображение проверено с использованием байесовской архитектуры связанных компонентов. Для обнаружения области интереса (ROI) применен алгоритм наиболее яркого пятна. Для обнаружения стадий диабетической ретинопатии по фотографиям глазного дна выполнены расчеты диска и чаши зрительного нерва. Изображения сегментированы от 0 до 4 с использованием архитектуры VGGNet16 и алгоритма SMOTE. Представленная модель с применением алгоритма ResNet на основе ансамбля с Efficient Net дала оценку точности 93 % и предсказанный коэффициент Каппа изображения (p < 0,01) 0,755 набора данных изображения сетчатки глазного дна.

The neuro-degenerative eye disease glaucoma is caused by an increase in eye pressure inside the retina. As the secondleading cause of blindness in the world, if an early diagnosis is not obtained, this can cause total blindness. Regarding this fundamental problem, there is a huge need to create a system that can function well without a lot of equipment, highly qualified medical personnel, and takes less time. The proposed modeling consists of three stages: pre-training, fine-tuning and inference. The probabilistic based pixel identification (Bayesian variant) predicts the severity of Diabetic Retinopathy (DR) which is diagnosed by the presence of visual cues, such as abnormal blood vessels, hard exudates, and cotton wool spots. The article combines machine learning, deep learning, and methods for image processing to predict the diagnosis images. The input picture is validated using Bayesian variant connected component architecture, and the brightest spot algorithm is applied to detect the Region of Interest (ROI). Moreover, the training sample calculated optic disc and optic cup are segmented with fundus photography ranges 0 to 4 using VGGNet16 architecture and SMOTE algorithm to detect DR stages of images and the proposed model using ensemble based ResNet with Efficient Net produces the excellent accuracy score of 93 % and predicted image Kappa coefficient (p < 0.01) 0.755 of the fundus retina image dataset.

байесовский вариантKeras и TensorFlowансамблевое обучениеEfficientNetResNetBayesian variantKeras and TensorFlowensemble learningEfficientNetResNetReferencesStitt A.W., Curtis T.M., Chen M., Medina R.J., McKay G.J., Jenkins A., Gardiner T.A., Lyons T.J., Hammes H.-P., Simó R., Lois N. The progress in understanding and treatment of diabetic retinopathy // Progress in Retinal and Eye Research. 2016. V. 51. P. 156–186. https://doi.org/10.1016/j.preteyeres.2015.08.001Stitt A.W., Curtis T.M., Chen M., Medina R.J., McKay G.J., Jenkins A., Gardiner T.A., Lyons T.J., Hammes H.-P., Simó R., Lois N. The progress in understanding and treatment of diabetic retinopathy. Progress in Retinal and Eye Research, 2016, vol. 51, pp. 156–186. https://doi.org/10.1016/j.preteyeres.2015.08.001Mahmoud M.H., Alamery S., Fouad H., Altinawi A., Youssef A.E. An automatic detection system of diabetic retinopathy using a hybrid inductive machine learning algorithm // Personal and Ubiquitous Computing. 2021. P. 1–15. https://doi.org/10.1007/s00779-020-01519-8Mahmoud M.H., Alamery S., Fouad H., Altinawi A., Youssef A.E. An automatic detection system of diabetic retinopathy using a hybrid inductive machine learning algorithm. Personal and Ubiquitous Computing, 2021, pp. 1–15. https://doi.org/10.1007/s00779-020-01519-8Dai L., Wu L., Li H., Cai C., Wu Q., Kong H., Liu R., Wang X., Hou X., Liu Y., Long X., Wen Y., Lu L., Shen Y., Chen Y., Shen D., Yang X., Zou H., Sheng B., Jia W. A deep learning system for detecting diabetic retinopathy across the disease spectrum // Nature Communications. 2021. V. 12. N 1. P. 3242. https://doi.org/10.1038/s41467-021-23458-5Dai L., Wu L., Li H., Cai C., Wu Q., Kong H., Liu R., Wang X., Hou X., Liu Y., Long X., Wen Y., Lu L., Shen Y., Chen Y., Shen D., Yang X., Zou H., Sheng B., Jia W. A deep learning system for detecting diabetic retinopathy across the disease spectrum. Nature Communications, 2021, vol. 12, no. 1, pp. 3242. https://doi.org/10.1038/s41467-021-23458-5Leung D.Y.L., Tham C.C. Normal-tension glaucoma: Current concepts and approaches — A review // Clinical & Experimental Ophthalmology. 2022. V. 50. N 2. P. 247–259. https://doi.org/10.1111/ceo.14043Leung D.Y.L., Tham C.C. Normal-tension glaucoma: Current concepts and approaches — A review. Clinical & Experimental Ophthalmology, 2022, vol. 50, no. 2, pp. 247–259. https://doi.org/10.1111/ceo.14043French J.A., Lawson J.A., Yapici Z., Ikeda H., Polster T., Nabbout R., Curatolo P., de Vries P.J., Dlugos D.J., Berkowitz N., Voi M., Peyrard S., Pelov D., Franz D.N. Adjunctive everolimus therapy for treatment-resistant focal-onset seizures associated with tuberous sclerosis (EXIST-3): a phase 3, randomised, double-blind, placebocontrolled study // The Lancet. 2016. V. 388. N 10056. P. 2153–2163. https://doi.org/10.1016/s0140-6736(16)31419-2French J.A., Lawson J.A., Yapici Z., Ikeda H., Polster T., Nabbout R., Curatolo P., de Vries P.J., Dlugos D.J., Berkowitz N., Voi M., Peyrard S., Pelov D., Franz D.N. Adjunctive everolimus therapy for treatment-resistant focal-onset seizures associated with tuberous sclerosis (EXIST-3): a phase 3, randomised, double-blind, placebocontrolled study. The Lancet, 2016, vol. 388, no. 10056, pp. 2153– 2163. https://doi.org/10.1016/s0140-6736(16)31419-2Nazir T., Nawaz M., Rashid J., Mahum R., Masood M., Mehmood A., Ali F., Kim J., Kwon H.-Y., Hussain A. Detection of diabetic eye disease from retinal images using a deep learning based CenterNet model // Sensors. 2021. V. 21. N 16. P. 5283. https://doi.org/10.3390/s21165283Nazir T., Nawaz M., Rashid J., Mahum R., Masood M., Mehmood A., Ali F., Kim J., Kwon H.-Y., Hussain A. Detection of diabetic eye disease from retinal images using a deep learning based CenterNet model. Sensors, 2021, vol. 21, no. 16, pp. 5283. https://doi.org/10.3390/s21165283Barros D., Moura J.C., Freire C.R., Taleb A.C., Valentim R.A., Morais P.S. Machine learning applied to retinal image processing for glaucoma detection: review and perspective // BioMedical Engineering OnLine. 2020. V. 19. N 1. P. 20. https://doi.org/10.1186/s12938-020-00767-2Barros D., Moura J.C., Freire C.R., Taleb A.C., Valentim R.A., Morais P.S. Machine learning applied to retinal image processing for glaucoma detection: review and perspective. BioMedical Engineering OnLine, 2020, vol. 19, no. 1, pp. 20. https://doi.org/10.1186/s12938-020-00767-2Ray J.S.R., Babu S.A., James J.W., Vedaiyan R. ARIMA based Time Series Analysis: Forecast COVID-19 Most Vaccinated Process and Active Cases classify using Probability Distribution Curve Rates (ARIMAPDC) // Proc. of the 2nd International Conference on Smart Electronics and Communication (ICOSEC). 2021. P. 546–551. https://doi.org/10.1109/ICOSEC51865.2021.9591774Ray J.S.R., Babu S.A., James J.W., Vedaiyan R. ARIMA based Time Series Analysis: Forecast COVID-19 Most Vaccinated Process and Active Cases classify using Probability Distribution Curve Rates (ARIMAPDC). Proc. of the 2nd International Conference on Smart Electronics and Communication (ICOSEC), 2021, pp. 546–551. https://doi.org/10.1109/ICOSEC51865.2021.9591774Raj R.J.S., Babu Anantha S., Jegatheesan A., Arul Xavier V.M. A GAN-based triplet FaceNet detection algorithm using deep face recognition for autism child // Lecture Notes in Electrical Engineering. 2022. V. 905. P. 177–187. https://doi.org/10.1007/978-981-19-2177-3_18Raj R.J.S., Babu Anantha S., Jegatheesan A., Arul Xavier V.M. A GAN-based triplet FaceNet detection algorithm using deep face recognition for autism child. Lecture Notes in Electrical Engineering, 2022, vol. 905, pp. 177–187. https://doi.org/10.1007/978-981-19-2177-3_18Babu S.A., Joshua Samuel Raj R., Varalatchoumy M., Gopila M., Febiyola Justin B.V. Novel approach for predicting COVID-19 symptoms using ARM based APRIORI algorithm // Proc. of the 6th International Conference on Computing Methodologies and Communication (ICCMC). 2022. P. 1577–1580. https://doi.org/10.1109/ICCMC53470.2022.9753987Babu S.A., Joshua Samuel Raj R., Varalatchoumy M., Gopila M., Febiyola Justin B.V. Novel approach for predicting COVID-19 symptoms using ARM based APRIORI algorithm. Proc. of the 6th International Conference on Computing Methodologies and Communication (ICCMC), 2022, pp. 1577–1580. https://doi.org/10.1109/ICCMC53470.2022.9753987Khojasteh P., Aliahmad B., Kumar D.K. A novel color space of fundus images for automatic exudates detection // Biomedical Signal Processing and Control. 2019. V. 49. P. 240–249. https://doi.org/10.1016/j.bspc.2018.12.004Khojasteh P., Aliahmad B., Kumar D.K. A novel color space of fundus images for automatic exudates detection. Biomedical Signal Processing and Control, 2019, vol. 49, pp. 240–249. https://doi.org/10.1016/j.bspc.2018.12.004Kouassi Nzoughet J., Guehlouz K., Leruez S., Gohier P., Bocca C., Muller J., Blanchet O., Bonneau D., Simard G., Milea D., Procaccio V., Lenaers G., de la Barca J.M.C., Reynier P. A data mining metabolomics exploration of glaucoma // Metabolites. 2020. V. 10. N 2. P. 49. https://doi.org/10.3390/metabo10020049Kouassi Nzoughet J., Guehlouz K., Leruez S., Gohier P., Bocca C., Muller J., Blanchet O., Bonneau D., Simard G., Milea D., Procaccio V., Lenaers G., de la Barca J.M.C., Reynier P. A data mining metabolomics exploration of glaucoma. Metabolites, 2020, vol. 10, no. 2, pp. 49. https://doi.org/10.3390/metabo10020049Pang R., Labisi S.A., Wang N. Pigment dispersion syndrome and pigmentary glaucoma: overview and racial disparities // Graefe’s Archive for Clinical and Experimental Ophthalmology. 2023. V. 261. N 3. P. 601–614. https://doi.org/10.1007/s00417-022-05817-0Pang R., Labisi S.A., Wang N. Pigment dispersion syndrome and pigmentary glaucoma: overview and racial disparities. Graefe’s Archive for Clinical and Experimental Ophthalmology, 2023, vol. 261, no. 3, pp. 601–614. https://doi.org/10.1007/s00417-022-05817-0Wang X., Zhao Y., Pourpanah F. Recent advances in deep learning // International Journal of Machine Learning and Cybernetics. 2020. V. 11. N 4. P. 747–750. https://doi.org/10.1007/s13042-020-01096-5Wang X., Zhao Y., Pourpanah F. Recent advances in deep learning. International Journal of Machine Learning and Cybernetics, 2020, vol. 11, no. 4, pp. 747–750. https://doi.org/10.1007/s13042-020-01096-5Domingues I., Pereira G., Martins P., Duarte H., Santos J., Abreu P.H. Using deep learning techniques in medical imaging: a systematic review of applications on CT and PET // Artificial Intelligence Review. 2020. V. 53. N 6. P. 4093–4160. https://doi.org/10.1007/s10462-019-09788-3Domingues I., Pereira G., Martins P., Duarte H., Santos J., Abreu P.H. Using deep learning techniques in medical imaging: a systematic review of applications on CT and PET. Artificial Intelligence Review, 2020, vol. 53, no. 6, pp. 4093–4160. https://doi.org/10.1007/s10462-019-09788-3Aloysius N., Geetha M. A review on deep convolutional neural networks // Proc. of the 2017 International Conference on Communication and Signal Processing (ICCSP). 2017. P. 0588–0592. https://doi.org/10.1109/iccsp.2017.8286426Aloysius N., Geetha M. A review on deep convolutional neural networks. Proc. of the 2017 International Conference on Communication and Signal Processing (ICCSP), 2017, pp. 0588– 0592. https://doi.org/10.1109/iccsp.2017.8286426Yu Z., Jiang X., Zhou F., Qin J., Ni D., Chen S., Wang T. Melanoma recognition in dermoscopy images via aggregated deep convolutional features // IEEE Transactions on Biomedical Engineering. 2019. V. 66. N 4. P. 1006–1016. https://doi.org/10.1109/tbme.2018.2866166Yu Z., Jiang X., Zhou F., Qin J., Ni D., Chen S., Wang T. Melanoma recognition in dermoscopy images via aggregated deep convolutional features. IEEE Transactions on Biomedical Engineering, 2019, vol. 66, no. 4, pp. 1006–1016. https://doi.org/10.1109/tbme.2018.2866166Ooi A.Z.H., Embong Z., Abd Hamid A.I., Zainon R., Wang S.L., Ng T.F., Hamzah R.A., Teoh S.S., Ibrahim H. Interactive blood vessel segmentation from retinal fundus image based on canny edge detector // Sensors. 2021. V. 21. N 19. P. 6380. https://doi.org/10.3390/s21196380Ooi A.Z.H., Embong Z., Abd Hamid A.I., Zainon R., Wang S.L., Ng T.F., Hamzah R.A., Teoh S.S., Ibrahim H. Interactive blood vessel segmentation from retinal fundus image based on canny edge detector. Sensors, 2021, vol. 21, no. 19, pp. 6380. https://doi.org/10.3390/s21196380Abdulsahib A.A., Mahmoud M.A., Mohammed M.A., Rasheed H.H., Mostafa S.A., Maashi M.S. Comprehensive review of retinal blood vessel segmentation and classification techniques: intelligent solutions for green computing in medical images, current challenges, open issues, and knowledge gaps in fundus medical images // Network Modeling Analysis in Health Informatics and Bioinformatics. 2021. V. 10. N 1. P. 1–32. https://doi.org/10.1007/s13721-021-00294-7Abdulsahib A.A., Mahmoud M.A., Mohammed M.A., Rasheed H.H., Mostafa S.A., Maashi M.S. Comprehensive review of retinal blood vessel segmentation and classification techniques: intelligent solutions for green computing in medical images, current challenges, open issues, and knowledge gaps in fundus medical images. Network Modeling Analysis in Health Informatics and Bioinformatics, 2021, vol. 10, no. 1, pp. 20. https://doi.org/10.1007/s13721-021-00294-7Artaechevarria X., Munoz-Barrutia A., Ortiz-de-Solorzano C. Combination strategies in multi-atlas image segmentation: application to brain MR data // IEEE Transactions on Medical Imaging. 2009. V. 28. N 8. P. 1266–1277. https://doi.org/10.1109/tmi.2009.2014372Artaechevarria X., Munoz-Barrutia A., Ortiz-de-Solorzano C. Combination strategies in multi-atlas image segmentation: application to brain MR data. IEEE Transactions on Medical Imaging, 2009, vol. 28, no. 8, pp. 1266–1277. https://doi.org/10.1109/tmi.2009.2014372Jia S., Jiang S., Lin Z., Li N., Xu M., Yu S. A survey: Deep learning for hyperspectral image classification with few labeled samples // Neurocomputing. 2021. V. 448. P. 179–204. https://doi.org/10.1016/j.neucom.2021.03.035Jia S., Jiang S., Lin Z., Li N., Xu M., Yu S. A survey: Deep learning for hyperspectral image classification with few labeled samples. Neurocomputing, 2021, vol. 448, pp. 179–204. https://doi.org/10.1016/j.neucom.2021.03.035Sazak Ç., Nelson C.J., Obara B. The multiscale bowler-hat transform for blood vessel enhancement in retinal images // Pattern Recognition. 2019. V. 88. P. 739–750. https://doi.org/10.1016/j.patcog.2018.10.011Sazak Ç., Nelson C.J., Obara B. The multiscale bowler-hat transform for blood vessel enhancement in retinal images. Pattern Recognition, 2019, vol. 88, pp. 739–750. https://doi.org/10.1016/j.patcog.2018.10.011Alshaikhli T., Liu W., Maruyama Y. Automated method of road extraction from aerial images using a deep convolutional neural network // Applied Sciences. 2019. V. 9. N 22. P. 4825. https://doi.org/10.3390/app9224825Alshaikhli T., Liu W., Maruyama Y. Automated method of road extraction from aerial images using a deep convolutional neural network. Applied Sciences, 2019, vol. 9, no. 22, pp. 4825. https://doi.org/10.3390/app9224825

The authors declare that there are no conflicts of interest present.