%0 Journal Article %1 noauthororeditor %A Vadivel, M. Mukesh Krishnan | J. Diofrin | M. %D 2023 %J INTERNATIONAL JOURNAL OF TREND IN SCIENTIFIC RESEARCH AND DEVELOPMENT %K Accuracy Convolutional Deep Diabetic Networks Neural Retinopathy Sensitivity Specificity %N 2 %P 557-565 %T Detection of Diabetic Retinopathy in Retinal Image Early Identification using Deep CNN %U https://www.ijtsrd.com.com/computer-science/other/55047/detection-of-diabetic-retinopathy-in-retinal-image-early-identification-using-deep-cnn/m-mukesh-krishnan %V 7 %X Diabetic Retinopathy the most common reason of vision loss is caused by damage to the small blood vessels in the retina. If untreated it may result in varying degrees of vision loss and even blindness. Since Diabetic Retinopathy is a silent disease that may cause no symptoms or only mild vision problems annual eye exams are crucial for early detection to improve the chances of effective treatment where fundus cameras are used to capture the retinal images. However fundus cameras are too big and heavy to be transported easily and too costly to be purchased by every health clinic so fundus cameras are an inconvenient tool for widespread screening. Recent technological developments have enabled using smartphones in designing small sized low power and affordable retinal imaging systems to perform Diabetic Retinopathy screening and automated Diabetic Retinopathy detection using machine learning and image processing methods. However Diabetic Retinopathy detection accuracy depends on the image quality and it is negatively affected by several factors such as Field of View. Since smartphone based retinal imaging systems have much more compact designs than the traditional fundus cameras the retina images captured are likely to be low quality with smaller Field of View As a result the smartphone based retina imaging systems can be used as an alternative to the direct ophthalmoscope once it tested in the clinical settings. However the Field of View of the smartphone based retina imaging systems plays an important role in determining the automatic Diabetic Retinopathy detection accuracy. M. Mukesh Krishnan | J. Diofrin | M. Vadivel "Detection of Diabetic Retinopathy in Retinal Image Early Identification using Deep CNN" Published in International Journal of Trend in Scientific Research and Development (ijtsrd) ISSN: 2456-6470 Volume-7 | Issue-2 April 2023 URL: https://www.ijtsrd.com.com/papers/ijtsrd55047.pdf Paper URL: https://www.ijtsrd.com.com/computer-science/other/55047/detection-of-diabetic-retinopathy-in-retinal-image-early-identification-using-deep-cnn/m-mukesh-krishnan

@article{noauthororeditor, abstract = {Diabetic Retinopathy the most common reason of vision loss is caused by damage to the small blood vessels in the retina. If untreated it may result in varying degrees of vision loss and even blindness. Since Diabetic Retinopathy is a silent disease that may cause no symptoms or only mild vision problems annual eye exams are crucial for early detection to improve the chances of effective treatment where fundus cameras are used to capture the retinal images. However fundus cameras are too big and heavy to be transported easily and too costly to be purchased by every health clinic so fundus cameras are an inconvenient tool for widespread screening. Recent technological developments have enabled using smartphones in designing small sized low power and affordable retinal imaging systems to perform Diabetic Retinopathy screening and automated Diabetic Retinopathy detection using machine learning and image processing methods. However Diabetic Retinopathy detection accuracy depends on the image quality and it is negatively affected by several factors such as Field of View. Since smartphone based retinal imaging systems have much more compact designs than the traditional fundus cameras the retina images captured are likely to be low quality with smaller Field of View As a result the smartphone based retina imaging systems can be used as an alternative to the direct ophthalmoscope once it tested in the clinical settings. However the Field of View of the smartphone based retina imaging systems plays an important role in determining the automatic Diabetic Retinopathy detection accuracy. M. Mukesh Krishnan | J. Diofrin | M. Vadivel "Detection of Diabetic Retinopathy in Retinal Image Early Identification using Deep CNN" Published in International Journal of Trend in Scientific Research and Development (ijtsrd) ISSN: 2456-6470 Volume-7 | Issue-2 April 2023 URL: https://www.ijtsrd.com.com/papers/ijtsrd55047.pdf Paper URL: https://www.ijtsrd.com.com/computer-science/other/55047/detection-of-diabetic-retinopathy-in-retinal-image-early-identification-using-deep-cnn/m-mukesh-krishnan }, added-at = {2023-07-11T12:17:21.000+0200}, author = {Vadivel, M. Mukesh Krishnan | J. Diofrin | M.}, biburl = {https://www.bibsonomy.org/bibtex/2e26df66d75e89408d5f401654497684e/ijtsrd}, interhash = {e1281ac956c0acc4dc6e985872f74cd5}, intrahash = {e26df66d75e89408d5f401654497684e}, issn = {2456-6470}, journal = {INTERNATIONAL JOURNAL OF TREND IN SCIENTIFIC RESEARCH AND DEVELOPMENT}, keywords = {Accuracy Convolutional Deep Diabetic Networks Neural Retinopathy Sensitivity Specificity}, language = {english}, month = apr, number = 2, pages = {557-565}, timestamp = {2023-07-11T12:17:21.000+0200}, title = {Detection of Diabetic Retinopathy in Retinal Image Early Identification using Deep CNN }, url = {https://www.ijtsrd.com.com/computer-science/other/55047/detection-of-diabetic-retinopathy-in-retinal-image-early-identification-using-deep-cnn/m-mukesh-krishnan}, volume = 7, year = 2023 }