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

Natasha Shaukat; Javeria Amin; Muhammad Sharif; Faisal Azam; Seifedine Kadry; Sujatha Krishnamoorthy

doi:10.3390/jpm12091454

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

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

Authors

Natasha Shaukat¹, Javeria Amin², Muhammad Sharif¹, Faisal Azam¹, Seifedine Kadry³, Sujatha Krishnamoorthy^{4

5}

Affiliations

¹ Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt 47010, Pakistan.
² Department of Computer Science, University of Wah, Wah Campus, Wah Cantt 47010, Pakistan.
³ Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway.
⁴ Zhejiang Bioinformatics International Science and Technology Cooperation Center, Wenzhou-Kean University, Wenzhou 325060, China.
⁵ Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics, Wenzhou-Kean University, Wenzhou 325060, China.

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.

Grants and funding

This research was funded by Wenzhou kean university grant number ICRP and The APC was funded by PI of the project sujatha krishnamoorthy.