Deep Learning Approaches for Diabetic Retinopathy Detection by Image Classification

Author:

Ali Mazumder Md. Sakib1,Hossain Tanzim1,Mehedi Shamrat F. M. Javed1,Jahan Nusrat2,Tasnim Zarrin1,Khater Ankit3

Affiliation:

1. Daffodil International University,Department of Software Engineering,Dhaka,Bangladesh

2. Universiti Malaysia Perlis (UniMAP),Department of Computer Engineering,Dhaka,Bangladesh

3. Jadavpur University,Department of Computer Science and Engineering,India

Publisher

IEEE

Reference27 articles.

1. An Improved Approach for Detection of Diabetic Retinopathy Using Feature Importance and Machine Learning Algorithms

2. Kaggle Dataset: Diabetic Retinopathy 224x224 Gaussian Filtered,2022

3. MobileNetV2: Inverted Residuals and Linear Bottlenecks;sandler,2018

4. Deep Residual Learning for Image Recognition

5. Rethinking the Inception Architecture for Computer Vision

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1. Human Activity Recognition a Comparison Between Residual Neural Network and Recurrent Neural Network;Lecture Notes in Networks and Systems;2024

2. Diabetic Retinopathy Detection Using Transfer Learning;AI and IoT-Based Technologies for Precision Medicine;2023-10-18

3. An Empirical Study of Deep Learning Techniques for Diabetic Retinopathy Detection;2023 3rd International Conference on Innovative Sustainable Computational Technologies (CISCT);2023-09-08

4. Identification of Suitable Machine Learning or Deep Learning Algorithmfor Diabetic Retinopathy Detection;2023 International Conference on Smart Systems for applications in Electrical Sciences (ICSSES);2023-07-07

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