Diabetic Retinopathy is a global health problem, influences 100 million individuals worldwide, and in the next few decades, these incidences are expected to reach epidemic proportions. Diabetic Retinopathy is a subtle eye disease that can cause sudden, irreversible vision loss. The early-stage Diabetic Retinopathy diagnosis can be challenging for human experts, considering the visual complexity of fundus photography retinal images. However, Early Stage detection of Diabetic Retinopathy can significantly alter the severe vision loss problem. The competence of computer-aided detection systems to accurately detect the Diabetic Retinopathy had popularized them among researchers. In this study, we have utilized a pre-trained DenseNet121 network with several modifications and trained on APTOS 2019 dataset. The proposed method outperformed other state-of-the-art networks in early-stage detection and achieved 96.51% accuracy in severity grading of Diabetic Retinopathy for multi-label classification and achieved 94.44% accuracy for single-class classification method. Moreover, the precision, recall, f1-score, and quadratic weighted kappa for our network was reported as 86%, 87%, 86%, and 91.96%, respectively. Our proposed architecture is simultaneously very simple, accurate, and efficient concerning computational time and space.
翻译:早期糖尿病视网膜病诊断对人类专家来说可能具有挑战性,因为Fundus摄影视网膜图像的视觉复杂性。然而,糖尿病视网膜病病早期检测可以大大改变严重的视力损失问题。计算机辅助检测系统准确检测糖尿病视网膜病情的能力已经使研究人员了解了这些病情。在这项研究中,我们利用了一个经过预先训练的DenseNet121网络,进行了若干次修改,并在APTOS 2019数据集上进行了培训。拟议的方法在早期检测中优于其他最先进的网络,实现了96.51%的精确度,在多标签分类中实现了糖尿病视网膜病病情,在单级分类方法中实现了94.44%的精确度。此外,精确度、回溯、F1和四重度重心眼病情检测系统已经在研究人员中普及了他们。在这项研究中,我们利用了经过预先训练的DenseNet121网络,并进行了若干次修改和关于APTOS 2019 数据集的培训。在早期检测中,拟议方法比其他最先进的网络更先进的网络精确的精确度网络,实现了96.51%的精确度分级计算,并且分别报告了关于我们空间87 %的精确的精确度和精确的计算。