A novel diabetic retinopathy grading using modified deep neural network with segmentation of blood vessels and retinal abnormalities

Sau, Paresh Chandra; Bansal, Atul

doi:10.1007/s11042-022-13056-y

Cited by 8 publications

(4 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deepa et al [18] and Deepa et al [19] have implemented a fine-tuned InceptionV3 and Xception based multi-stage patch-based and image-based CNN and have achieved an accuracy of 96.2% and 96%, respectively using a computationally powerful [16] have implemented Squeeze-and-Excitation CNN using limited DIARETDB1 and local dataset and achieved an accuracy of 96.92% whereas DRFEC has consistently achieved an accuracy of more than 98% and 70% on 35,126 images. Sau and Bansal [70] have implemented a FNU-GOA-MDNN for optimization using a comparatively limited ISBI 2018 IDRiD dataset and have achieved an accuracy of 95.27%, whereas DRFEC has consistently achieved an accuracy of more than 98% and 70% on 35,126 images. Shaik and Cherukuri [72] have implemented a VGG-16 based HA-Net using 3662 Kaggle's APTOS 2019 images and 2018 IDRiD images, and achieved an accuracy 85.54% and 66.41% respectively, whereas DRFEC has achieved an accuracy of 90.91% and 62.07% using VGG-16 and 97.98% and 73.37% using VGG-19 respectively on 35,126 images.…”

Section: Discussionmentioning

confidence: 99%

“…It uses 1, 40,000 fundus images from publicly available EyePACs dataset and 1200 fundus images from Shanghai General Hospital. Deepa et al [19] Sau and Bansal [70] have proposed a Fitness based Newly Updated Grasshopper Optimization Algorithm (FNU-GOA) for the optimization of a DL model and to optimize the threshold value in active contour method for the segmentation of blood vessels, MAs, EXs and HEs for DR detection. It is compared with several other optimization algorithms such as Particle Swarm Optimization (PSO), Grey wolf optimization algorithm (GWO), Whale optimization algorithm (WOA) and Grasshopper Optimization Algorithm (GOA), and ML classifiers such as Neural Network (NN), RNN, Long Short Term Memory (LSTM) and Deep NN.…”

Section: The Proposed Model Identifies High Bias and High Variance Du...mentioning

confidence: 99%

“…On this basis we reject our null hypothesis which claims on choosing and finding a best DL model for image classification, and accept our alternative hypothesis of finding and engineering a better DL model for DR image classification. Figures 61 and 62 depicts the plot of KSPA statistical test for 26 DL models, and comparison of them with contemporary and state-of-the-art works [1, 16,18,19,23,70,72,82], respectively.…”

Section: Inceptionv3mentioning

confidence: 99%

See 2 more Smart Citations

Detection of Diabetic Retinopathy using Convolutional Neural Networks for Feature Extraction and Classification (DRFEC)

Das

Biswas

Bandyopadhyay

2022

Multimed Tools Appl

View full text Add to dashboard Cite

Diabetic Retinopathy (DR) is caused as a result of Diabetes Mellitus which causes development of various retinal abrasions in the human retina. These lesions cause hindrance in vision and in severe cases, DR can lead to blindness. DR is observed amongst 80% of patients who have been diagnosed from prolonged diabetes for a period of 10–15 years. The manual process of periodic DR diagnosis and detection for necessary treatment, is time consuming and unreliable due to unavailability of resources and expert opinion. Therefore, computerized diagnostic systems which use Deep Learning (DL) Convolutional Neural Network (CNN) architectures, are proposed to learn DR patterns from fundus images and identify the severity of the disease. This paper proposes a comprehensive model using 26 state-of-the-art DL networks to assess and evaluate their performance, and which contribute for deep feature extraction and image classification of DR fundus images. In the proposed model, ResNet50 has shown highest overfitting in comparison to Inception V3, which has shown lowest overfitting when trained using the Kaggle’s EyePACS fundus image dataset. EfficientNetB4 is the most optimal, efficient and reliable DL algorithm in detection of DR, followed by InceptionResNetV2, NasNetLarge and DenseNet169. EfficientNetB4 has achieved a training accuracy of 99.37% and the highest validation accuracy of 79.11%. DenseNet201 has achieved the highest training accuracy of 99.58% and a validation accuracy of 76.80% which is less than the top-4 best performing models.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: The Proposed Model Identifies High Bias and High Variance Du...mentioning

confidence: 99%

Section: Inceptionv3mentioning

confidence: 99%

See 1 more Smart Citation

Detection of Diabetic Retinopathy using Convolutional Neural Networks for Feature Extraction and Classification (DRFEC)

Das

Biswas

Bandyopadhyay

2022

Multimed Tools Appl

View full text Add to dashboard Cite

show abstract

“…These models can then be deployed on edge devices or cloud platforms to provide real-time classification results, allowing for prompt medical interventions when necessary. Machine learning algorithms [7] can be trained on large datasets of labelled retinal images to develop accurate classification models for DR. These models learn from patterns and features present in the data, enabling them to distinguish between healthy retinas and those affected by DR.…”

Section: Introductionmentioning

confidence: 99%

DRGNet: Diabetic Retinopathy Grading Network Using Data Balancing Integrated Transfer Learning with Graph-based KNN Classification

2023

IJIES

View full text Add to dashboard Cite

Diabetic retinopathy (DR) is a prevalent and potentially blinding eye disease that affects individuals with diabetes. On the other hand, internet of medical things (IoMT) incorporation into DR grading classification offers a promising future for the field of eye health. It optimizes the diagnostic process, increases accessibility to healthcare services, and ultimately improves patient care and outcomes. Therefore, accurate and timely grading of DR severity is crucial for effective disease management. In this article, DR-grading network (DRG-Net) is proposed, which is a comprehensive approach for DR labels classification using the indian diabetic retinopathy image dataset (IDRiD) dataset. To address the imbalanced nature of the dataset, synthetic minority over-sampling technique (SMOTE) is employed for data balancing, ensuring representative samples for each severity level. Then, transfer learning based residual network-50 (ResNet50) architecture is used to extract features from SMOTE outcome, which is a deep learning model renowned for its ability to learn complex image representations. Finally, graph-based K-nearest neighbours (GKNN) classification which utilizes the spatial relationships between samples to make informed decisions, considering the similarity of retinal images in a graph-based representation is introduced for enhanced grading classification of DR. The simulation results show that, the proposed DRG-Net resulted in improved performance as compared to state-of-the-art approaches such as MSA-ResNetGB, DLCNN-MGWO-VW, OHGCNet, and E-DenseNet BC-121 with an accuracy of 99.93%, and F1-score of 99.85%.

show abstract

Joint Skeleton and Boundary Features Networks for Curvilinear Structure Segmentation

Wang

Chen

Feng

et al. 2023

Lecture Notes in Computer Science

View full text Add to dashboard Cite

A novel diabetic retinopathy grading using modified deep neural network with segmentation of blood vessels and retinal abnormalities

Cited by 8 publications

References 47 publications

Detection of Diabetic Retinopathy using Convolutional Neural Networks for Feature Extraction and Classification (DRFEC)

Detection of Diabetic Retinopathy using Convolutional Neural Networks for Feature Extraction and Classification (DRFEC)

DRGNet: Diabetic Retinopathy Grading Network Using Data Balancing Integrated Transfer Learning with Graph-based KNN Classification

Joint Skeleton and Boundary Features Networks for Curvilinear Structure Segmentation

Contact Info

Product

Resources

About