Current eye research · 2025
PCANN: Principal Convolutional Analysis Neural Network for Block Chain based Diabetic Retinopathy Detection.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Krishnamoorthy Somasundaram, Paulraj Sivakumar, Periyasamy Baskaran, Ramamoorthy Arun Kumar
Аннотация
PURPOSE: Diabetic retinopathy is an ophthalmic disease that impairs the retinal blood vessels. Diabetic retinopathy can lead to blindness when it is not examined in earlier phases. Adversely, the accurate diabetic retinopathy recognition phase is prominently complicated and needs experienced human analysis of fundus images. Blockchain technology helps share data by allowing users to select what information to share and control who can access it, which is important for managing electronic health records in healthcare sector. Nevertheless, the privacy of user data is compromised due to the training data, which is revealed to unauthorized users.
METHODS: In this work, a superior module for diabetic retinopathy classification based on Blockchain using principal convolutional analysis neural network is designed. Here, the simulation of Blockchain is carried out. Here, the input image is pre-processed using the Gaussian filter. LadderNet is deployed for lesion segmentation, and the segmentation of blood vessel is done using the Sine-Net model. Moreover, feature extraction is performed with the input image, lesion-segmented image, and blood vessel-segmented image. Finally, diabetic retinopathy classification is executed utilizing the proposed principal convolutional analysis neural network, which is classified into normal, mild non-proliferative diabetic retinopathy, moderate non-proliferative diabetic retinopathy, severe non-proliferative diabetic retinopathy, and proliferative.
RESULTS: The Blockchain enabled principal convolutional analysis neural network obtains superior values of 90.9%, 91.9%, 92.5%, 89.4%, 88.4%, and 75.5% in terms of metrics like accuracy, true positive rate, true negative rate, positive predictive value, negative predictive value, and Mathews correlation coefficient.
CONCLUSION: The integration of principal convolutional analysis neural network with Blockchain enhances data integrity and patient privacy, making it a promising solution for early diagnosis and treatment. Also, this approach ensures accurate and efficient detection of diabetic retinopathy.
METHODS: In this work, a superior module for diabetic retinopathy classification based on Blockchain using principal convolutional analysis neural network is designed. Here, the simulation of Blockchain is carried out. Here, the input image is pre-processed using the Gaussian filter. LadderNet is deployed for lesion segmentation, and the segmentation of blood vessel is done using the Sine-Net model. Moreover, feature extraction is performed with the input image, lesion-segmented image, and blood vessel-segmented image. Finally, diabetic retinopathy classification is executed utilizing the proposed principal convolutional analysis neural network, which is classified into normal, mild non-proliferative diabetic retinopathy, moderate non-proliferative diabetic retinopathy, severe non-proliferative diabetic retinopathy, and proliferative.
RESULTS: The Blockchain enabled principal convolutional analysis neural network obtains superior values of 90.9%, 91.9%, 92.5%, 89.4%, 88.4%, and 75.5% in terms of metrics like accuracy, true positive rate, true negative rate, positive predictive value, negative predictive value, and Mathews correlation coefficient.
CONCLUSION: The integration of principal convolutional analysis neural network with Blockchain enhances data integrity and patient privacy, making it a promising solution for early diagnosis and treatment. Also, this approach ensures accurate and efficient detection of diabetic retinopathy.