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. 2022 Sep 5;12(9):1454.
doi: 10.3390/jpm12091454.

Three-Dimensional Semantic Segmentation of Diabetic Retinopathy Lesions and Grading Using Transfer Learning

Natasha Shaukat  1 , Javeria Amin  2 , Muhammad Sharif  1 , Faisal Azam  1 , Seifedine Kadry  3 , Sujatha Krishnamoorthy  4   5
Affiliations

Three-Dimensional Semantic Segmentation of Diabetic Retinopathy Lesions and Grading Using Transfer Learning

Natasha Shaukat et al. J Pers Med. .

Abstract

Diabetic retinopathy (DR) is a drastic disease. DR embarks on vision impairment when it is left undetected. In this article, learning-based techniques are presented for the segmentation and classification of DR lesions. The pre-trained Xception model is utilized for deep feature extraction in the segmentation phase. The extracted features are fed to Deeplabv3 for semantic segmentation. For the training of the segmentation model, an experiment is performed for the selection of the optimal hyperparameters that provided effective segmentation results in the testing phase. The multi-classification model is developed for feature extraction using the fully connected (FC) MatMul layer of efficient-net-b0 and pool-10 of the squeeze-net. The extracted features from both models are fused serially, having the dimension of N × 2020, amidst the best N × 1032 features chosen by applying the marine predictor algorithm (MPA). The multi-classification of the DR lesions into grades 0, 1, 2, and 3 is performed using neural network and KNN classifiers. The proposed method performance is validated on open access datasets such as DIARETDB1, e-ophtha-EX, IDRiD, and Messidor. The obtained results are better compared to those of the latest published works.

Keywords: DR; Messidor; convolutional neural network; deeplabv3; lesions.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Symptoms of NPDR lesions [9]: (a) HEs (b) HMs (c) MAs (d) OD (e) SoEX.
Figure 2
Figure 2
Steps of proposed method for segmentation and classification.
Figure 3
Figure 3
Proposed model for NPDR lesion segmentation.
Figure 4
Figure 4
Best selected features using MPA.
Figure 5
Figure 5
Segmented EX lesions of the e-ophtha-EX dataset. (a) Input image, (b) proposed segmentation, (c) ground truth image, (d) segmented region mapped on the input image.
Figure 6
Figure 6
Segmented DR lesion outcomes on DIARETDB1 dataset. (a) Input image, (b) proposed segmentation, (c) ground truth image, (d) segmented region mapped on the input image.
Figure 7
Figure 7
Segmented DR lesions on IDRiD dataset. (a) Input image, (b) proposed segmentation, (c) ground truth image, (d) segmented region mapped on the input image.
Figure 8
Figure 8
Classification of DR lesions. (a) KNN of the optimizable kernel, (b) NN of medium neural network.
Figure 9
Figure 9
Confidence interval of classification accuracy by performing Monte Carlo simulation on 10-fold cross-validation using Messidor dataset.
Figure 10
Figure 10
Confidence interval of classification accuracy by performing Monte Carlo simulation on 15 iterations using Messidor dataset.
Figure 11
Figure 11
Confidence interval of classification accuracy by performing Monte Carlo simulation on 20 iterations using Messidor dataset.
See this image and copyright information in PMC

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