Deep Learning based Multi-Modal Algorithms for the Prediction of Diabetic Retinopathy
M.Sangeetha
, K.Hariharasubramaniyan , K.Dinesh , R.Amarnath
CNN, Diabetic Retinopathy (DR), ResNet, VGG16, GoogleNet, InceptionV3, and Fundus Retinal image
Diabetic retinopathy (DR) is a prevalent and debilitating condition that affects millions of people worldwide. It is an eye complication that can occur in individuals with diabetes, resulting from the impairment of retinal blood vessels, which is the light-sensitive layer positioned at the back of the eye. Timely detection and management of DR are pivotal to prevent loss of vision and improve patient outcomes. Here, a deep learning-based algorithm for DR detection that uses only fundus photographs is proposed. The proposed algorithm trains and tests five deep learning models - Convolutional Neural Network (CNN), ResNet, GoogleNet, InceptionV3, and VGG16, to classify the severity of DR. To assess the effectiveness of the suggested algorithms, a dataset consisting of 3662 fundus retinal images obtained from patients with and without diabetic retinopathy
was utilized. The images were divided into five categories based on DR severity: No DR, mild, moderate, severe, and proliferate. The dataset is analyzed using the stated Deep Learning Algorithms to determine the optimum performance in terms of accuracy, training time, recall, prediction time, test score, precision and the F1 score. The best algorithm is chosen after each one’s overall performance has been assessed.
Experimental results show that the best algorithm achieves an accuracy of 96% in DR detection, which is higher than the accuracy achieved by other single-modal algorithms.
Furthermore, the algorithm is robust to noise and variability in the images, indicating its potential for clinical use.
"Deep Learning based Multi-Modal Algorithms for the Prediction of Diabetic Retinopathy", TIJER - TIJER - INTERNATIONAL RESEARCH JOURNAL (www.TIJER.org), ISSN:2349-9249, Vol.11, Issue 2, page no.580-589, February-2024, Available :https://tijer.org/TIJER/papers/TIJERB001092.pdf
Volume 11
Issue 2,
February-2024
Pages : 580-589
Paper Reg. ID: TIJER_151018
Published Paper Id: TIJERB001092
Downloads: 000280
Research Area: Science and Technology
Country: Thiruvallur, Chennai, Tamilnadu, India
ISSN: 2349-9249 | IMPACT FACTOR: 8.57 Calculated By Google Scholar | ESTD YEAR: 2014
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.57 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: TIJER (IJ Publication) Janvi Wave
