Author:
Duraichi N.,Jalaja S.,Merlin C.D.,Meena J.S.,Kamali R.N.,Manoj K.
Abstract
Detection of Diabetic Retinopathy at the early stages could significantly reduce the need for complicated and expensive surgeries. The availability of large datasets has fuelled research in this field. In this project, diabetic retinopathy is detected and classified into five stages: no DR, severe DR, Moderate DR, Proliferative DR, and mild DR. This is made possible with the help of various deep learning techniques. A trained model (ResNet-50 architecture) is used for the extraction of various features from the images. This model gives an accuracy of 0.47% in testing. The dataset used is the Aptos 2019 dataset which is available on Kaggle.
Subject
Economics and Econometrics,Education,Earth-Surface Processes,Geography, Planning and Development,Clinical Biochemistry,General Biochemistry, Genetics and Molecular Biology,Molecular Biology,General Chemistry,Linguistics and Language,Language and Linguistics,General Medicine,Computer Science Applications,Linguistics and Language,Education,Language and Linguistics,History,Cultural Studies,Physiology,Literature and Literary Theory,Linguistics and Language,Language and Linguistics,Cultural Studies
Cited by
2 articles.
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