# Blended Multi-Modal Deep ConvNet Features for Diabetic Retinopathy Severity Prediction

arXiv: https://arxiv.org/abs/2006.00197 · HTML: https://mlsanity.com/paper/2006.00197/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2020.

## Built on by (most cited first, 13 of 13)

- [Topological Signatures vs. Gradient Histograms: A Comparative Study for Medical Image Classification](https://mlsanity.com/paper/2507.03006/) (2025)
- [HOG-CNN: Integrating Histogram of Oriented Gradients with Convolutional Neural Networks for Retinal Image Classification](https://mlsanity.com/paper/2507.22274/) (2025)
- [Dual Branch Deep Learning Network for Detection and Stage Grading of Diabetic Retinopathy](https://mlsanity.com/paper/2308.09945/) (2023)
- [Addressing High Class Imbalance in Multi-Class Diabetic Retinopathy Severity Grading with Augmentation and Transfer Learning](https://mlsanity.com/paper/2507.17121/) (2025)
- [Multitasking Deep Learning Model for Detection of Five Stages of Diabetic Retinopathy](https://mlsanity.com/paper/2103.04207/) (2021)
- [Integrating Non-Linear Radon Transformation for Diabetic Retinopathy Grading](https://mlsanity.com/paper/2504.15883/) (2025)
- [A systematic review of transfer learning based approaches for diabetic retinopathy detection](https://mlsanity.com/paper/2105.13793/) (2021)
- [Taming the Tail: Leveraging Asymmetric Loss and Pade Approximation to Overcome Medical Image Long-Tailed Class Imbalance](https://mlsanity.com/paper/2410.04084/) (2024)
- [Early Diagnosis of Retinal Blood Vessel Damage via Deep Learning-Powered Collective Intelligence Models](https://mlsanity.com/paper/2210.09449/) (2022)
- [Transfer-Ensemble Learning based Deep Convolutional Neural Networks for Diabetic Retinopathy Classification](https://mlsanity.com/paper/2308.00525/) (2023)
- [Introducing Feature Attention Module on Convolutional Neural Network for Diabetic Retinopathy Detection](https://mlsanity.com/paper/2308.02985/) (2023)
- [Early detection of diabetes through transfer learning-based eye (vision) screening and improvement of machine learning model performance and advanced parameter setting algorithms](https://mlsanity.com/paper/2504.03439/) (2025)
- [Diabetic Retinopathy Detection Based on Convolutional Neural Networks with SMOTE and CLAHE Techniques Applied to Fundus Images](https://mlsanity.com/paper/2504.05696/) (2025)

## Abstract (arXiv)

Diabetic Retinopathy (DR) is one of the major causes of visual impairment and blindness across the world. It is usually found in patients who suffer from diabetes for a long period. The major focus of this work is to derive optimal representation of retinal images that further helps to improve the performance of DR recognition models. To extract optimal representation, features extracted from multiple pre-trained ConvNet models are blended using proposed multi-modal fusion module. These final representations are used to train a Deep Neural Network (DNN) used for DR identification and severity level prediction. As each ConvNet extracts different features, fusing them using 1D pooling and cross pooling leads to better representation than using features extracted from a single ConvNet. Experimental studies on benchmark Kaggle APTOS 2019 contest dataset reveals that the model trained on proposed blended feature representations is superior to the existing methods. In addition, we notice that cross average pooling based fusion of features from Xception and VGG16 is the most appropriate for DR recognition. With the proposed model, we achieve an accuracy of 97.41%, and a kappa statistic of 94.82 for DR identification and an accuracy of 81.7% and a kappa statistic of 71.1% for severity level prediction. Another interesting observation is that DNN with dropout at input layer converges more quickly when trained using blended features, compared to the same model trained using uni-modal deep features.