A Deep Learning Approach for Efficient Eye Influenza Detection

• Author(s): Dr. Santosh Singh ; Sairaj Uday Ghag ; Vrushali Shriniwas Bagve
• Paper ID: 1705367
• Page: 82-87
• Published Date: 08-01-2024
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 7 Issue 7 January-2024

Abstract

Eye flu, a prevalent infection, poses significant health risks and demands accurate and timely diagnosis for effective management. This research introduces EyeFluNet, a novel Convolutional Neural Network (CNN) model designed for precise and rapid detection of eye influenza from ocular images. Leveraging deep learning, EyeFluNet employs a multi-layered architecture for feature extraction and classification, capturing intricate patterns indicative of flu infection. The model's robustness is manifested through a series of convolutional and pooling layers, enabling the recognition of subtle flu-related manifestations within eye images. Trained and validated on diverse datasets, EyeFluNet showcases a remarkable 88% accuracy in distinguishing between flu-infected and healthy eyes. This high accuracy, coupled with its computational efficiency, demonstrates the potential of EyeFluNet as a reliable tool for early-stage flu identification. The implementation of EyeFluNet holds promise for facilitating prompt intervention, reducing transmission rates, and enhancing healthcare strategies in combating ocular flu infections.

Keywords

Eye Influenza, Ocular Infection, Deep Learning, Convolutional Neural Networks, Image Analysis, Diagnosis

Citations

IRE Journals:
Dr. Santosh Singh , Sairaj Uday Ghag , Vrushali Shriniwas Bagve "A Deep Learning Approach for Efficient Eye Influenza Detection" Iconic Research And Engineering Journals Volume 7 Issue 7 2024 Page 82-87

IEEE:
Dr. Santosh Singh , Sairaj Uday Ghag , Vrushali Shriniwas Bagve "A Deep Learning Approach for Efficient Eye Influenza Detection" Iconic Research And Engineering Journals, 7(7)