Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi, 6204, Bangladesh.
Department of Computer Science, Cihan University Sulaimaniya, Sulaimaniya, 46001, Kurdistan Region, Iraq.
Comput Biol Med. 2022 Jul;146:105602. doi: 10.1016/j.compbiomed.2022.105602. Epub 2022 May 10.
Diabetic Retinopathy (DR) is a major complication in human eyes among the diabetic patients. Early detection of the DR can save many patients from permanent blindness. Various artificial intelligent based systems have been proposed and they outperform human analysis in accurate detection of the DR. In most of the traditional deep learning models, the cross-entropy is used as a common loss function in a single stage end-to-end training method. However, it has been recently identified that this loss function has some limitations such as poor margin leading to false results, sensitive to noisy data and hyperparameter variations. To overcome these issues, supervised contrastive learning (SCL) has been introduced. In this study, SCL method, a two-stage training method with supervised contrastive loss function was proposed for the first time to the best of authors' knowledge to identify the DR and its severity stages from fundus images (FIs) using "APTOS 2019 Blindness Detection" dataset. "Messidor-2" dataset was also used to conduct experiments for further validating the model's performance. Contrast Limited Adaptive Histogram Equalization (CLAHE) was applied for enhancing the image quality and the pre-trained Xception CNN model was deployed as the encoder with transfer learning. To interpret the SCL of the model, t-SNE method was used to visualize the embedding space (unit hyper sphere) composed of 128 D space into a 2 D space. The proposed model achieved a test accuracy of 98.36%, and AUC score of 98.50% to identify the DR (Binary classification) and a test accuracy of 84.364%, and AUC score of 93.819% for five stages grading with the APTOS 2019 dataset. Other evaluation metrics (precision, recall, F1-score) were also determined with APTOS 2019 as well as with Messidor-2 for analyzing the performance of the proposed model. It was also concluded that the proposed method achieved better performance in detecting the DR compared to the conventional CNN without SCL and other state-of-the-art methods.
糖尿病性视网膜病变（DR）是糖尿病患者眼部的主要并发症。DR 的早期检测可以使许多患者免于永久性失明。已经提出了各种基于人工智能的系统，它们在准确检测 DR 方面优于人类分析。在大多数传统的深度学习模型中，交叉熵被用作单阶段端到端训练方法中的常见损失函数。然而，最近已经确定，该损失函数存在一些局限性，例如导致错误结果的较差边界、对嘈杂数据和超参数变化敏感。为了克服这些问题，引入了监督对比学习（SCL）。在这项研究中，首次提出了 SCL 方法，这是一种具有监督对比损失函数的两阶段训练方法，用于根据“APTOS 2019 盲目性检测”数据集从眼底图像（FI）中识别 DR 及其严重程度阶段。还使用“Messidor-2”数据集进行实验以进一步验证模型的性能。对比度受限自适应直方图均衡化（CLAHE）用于增强图像质量，并部署预先训练的 Xception CNN 模型作为具有迁移学习的编码器。为了解释模型的 SCL，使用 t-SNE 方法将由 128 D 空间组成的嵌入空间（单位超球体）可视化到 2 D 空间中。所提出的模型在识别 DR（二进制分类）时达到了 98.36％的测试精度和 98.50％的 AUC 得分，在使用 APTOS 2019 数据集进行五级分级时达到了 84.364％的测试精度和 93.819％的 AUC 得分。还使用 APTOS 2019 以及 Messidor-2 确定了其他评估指标（精度、召回率、F1 分数），以分析所提出模型的性能。还得出结论，与没有 SCL 的传统 CNN 以及其他最先进的方法相比，所提出的方法在检测 DR 方面表现更好。
