Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
This paper proposes a neural network model for segmenting blood vessels in retinal images. The model is based on the CNN module and uses dense blocks to replace the traditional U-net connections. This allows for better feature fusion from different layers of the network. Retinal vessel segmentation is an important tool for medical screening and diagnosis of various diseases. The method presented in this project contains a deep convolutional neural network (CNN) that scans the captured or uploaded image and proceeds with the dataset to show the accruable result. The smart eyes health detection application is allowed to detect the health of the eyes by capturing the image of the eyes and process inside the machine/app to compare with the dataset and the result to show the output and generate a report with the accuracy of our eyes health condition. The generated image will pass by the deep learning process with the accuracy of the result between 80% to 85% through the CNN.
"SEHD - Smart Eyes Health Detection", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 4, page no.684 - 689, April-2023, Available :http://www.ijrti.org/papers/IJRTI2304114.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator