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Related Experiment Video

Updated: Jul 24, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Using Deep Learning Architectures for Detection and Classification of Diabetic Retinopathy.

Cheena Mohanty¹, Sakuntala Mahapatra², Biswaranjan Acharya³

¹Department of Electronics and Telecommunication, Biju Patnaik University of Technology, Rourkela 769012, Odisha, India.

Sensors (Basel, Switzerland)

|July 8, 2023

Summary

This summary is machine-generated.

Deep learning models accurately detect diabetic retinopathy (DR), a leading cause of blindness. DenseNet 121 achieved 97.30% accuracy, outperforming other methods for early DR detection.

Keywords:

DenseNet 121 VGG16 XGBoost classifier convolutional neural networks data balance diabetic retinopathy

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Diabetic retinopathy (DR) is a severe complication of diabetes, potentially causing irreversible blindness.
Early DR detection is critical for timely intervention, but manual grading of retinal images is inefficient and error-prone.
Automated methods are needed to improve the accuracy and efficiency of DR diagnosis.

Purpose of the Study:

To develop and evaluate deep learning (DL) models for the automated detection and classification of diabetic retinopathy (DR).
To compare the performance of a hybrid VGG16-XGBoost network with the DenseNet 121 architecture for DR classification.
To address class imbalance in retinal image datasets for improved model generalizability.

Main Methods:

Two DL architectures were proposed: a hybrid VGG16-XGBoost classifier and the DenseNet 121 network.
Retinal images from the APTOS 2019 Blindness Detection Kaggle Dataset were preprocessed and utilized for model training and evaluation.
Class balancing techniques were applied to mitigate the impact of imbalanced data distribution.

Main Results:

The hybrid VGG16-XGBoost model achieved an accuracy of 79.50% in DR classification.
The DenseNet 121 model demonstrated superior performance with an accuracy of 97.30%.
Comparative analysis confirmed the enhanced efficacy of DenseNet 121 over existing methods on the same dataset.

Conclusions:

Deep learning architectures, particularly DenseNet 121, show significant potential for accurate and efficient early detection and classification of diabetic retinopathy.
Automated DR diagnosis systems can enhance diagnostic efficiency and accuracy, benefiting patient outcomes and healthcare systems.
The DenseNet 121 model represents a promising tool for clinical application in ophthalmology.