Shaukat Natasha, Amin Javeria, Sharif Muhammad, Azam Faisal, Kadry Seifedine, Krishnamoorthy Sujatha
Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt 47010, Pakistan.
Department of Computer Science, University of Wah, Wah Campus, Wah Cantt 47010, Pakistan.
J Pers Med. 2022 Sep 5;12(9):1454. doi: 10.3390/jpm12091454.
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.
糖尿病视网膜病变（DR）是一种严重的疾病。如果未被发现，DR会导致视力受损。在本文中，提出了基于学习的技术用于DR病变的分割和分类。在分割阶段，使用预训练的Xception模型进行深度特征提取。提取的特征被输入到Deeplabv3进行语义分割。为了训练分割模型，进行了一项实验以选择在测试阶段能提供有效分割结果的最优超参数。使用高效网络b0的全连接（FC）MatMul层和挤压网络的池化层10开发多分类模型用于特征提取。从两个模型中提取的特征按顺序融合，维度为N×2020，同时通过应用海洋预测算法（MPA）从最佳的N×1032个特征中进行选择。使用神经网络和KNN分类器将DR病变多分类为0级、1级、2级和3级。所提出的方法在诸如DIARETDB1、e-ophtha-EX、IDRiD和Messidor等开放获取数据集上进行了性能验证。与最新发表的作品相比，所获得的结果更好。
