Deep Feature Extraction and Classification of Diabetic Retinopathy Using AlexNet, InceptionV3, and VGG16 CNN Architectures

DOI

10.2991/978-94-6463-612-3_7How to use a DOI?

Keywords

AlexNet; Convolutional Neural Networks; Diabetic Retinopathy InceptionV3; VGG16

Abstract

Diabetic retinopathy (DR) is a significant cause of vision impairment and blindness among diabetic patients, characterized by progressive retinal damage. Early and accurate detection is crucial for effective management and treatment. This research explores advanced deep learning techniques to enhance DR detection and classification by leveraging Convolutional Neural Networks (CNNs). We propose a novel methodology incorporating deep feature extraction and classification using three CNN architectures: AlexNet, InceptionV3, and VGG16. Our approach involves extracting deep features from retinal images to capture intricate patterns associated with various DR stages, followed by classification to differentiate between healthy and various stages of DR. The dataset used include publicly available Fundus Image Registration Dataset (FIRE) for comprehensive evaluation. Detailed preprocessing steps ensured data quality and relevance, while feature extraction techniques harnessed the strengths of the selected CNN architectures. The performance of the proposed models was evaluated based on accuracy, sensitivity, precision, and F1-score. Our results demonstrate that AlexNet achieves the highest accuracy at 95.37%, outperforming InceptionV3 and VGG16. This study underscores the effectiveness of CNN-based approaches in DR detection and highlights the potential for further improvements in early diagnosis and treatment strategies.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Shubhi Shrivastava
AU  - Shanti Rathore
AU  - Rahul Gedam
PY  - 2024
DA  - 2024/12/31
TI  - Deep Feature Extraction and Classification of Diabetic Retinopathy Using AlexNet, InceptionV3, and VGG16 CNN Architectures
BT  - Proceedings of the 5th International Conference on the Role of Innovation, Entrepreneurship and Management for Sustainable Development (ICRIEMSD-2024)
PB  - Atlantis Press
SP  - 80
EP  - 94
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-612-3_7
DO  - 10.2991/978-94-6463-612-3_7
ID  - Shrivastava2024
ER  -