Empowering Diabetic Eye Disease Detection: Leveraging Differential Evolution for Optimized Convolution Neural Networks

Rahul Ray, Sudarson Jena, Priyadarsan Parida, Laxminarayan Dash, Sangita Kumari Biswal
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Abstract

Diabetic eye detection has become a major concern across the globe, which could be effectively addressed by automated detection using a deep convolutional neural network (DCNN). CNN models have better detection and classification accuracy than other state-of-theart models. In this paper, a differential evolution (DE)-optimized CNN has been proposed for the single-step classification of diabetic retinopathy (DR) and glaucoma images. DE has been used to find out the optimized values of four hyper-parameters of CNN, i.e., the number of filters in the first layer, the filter size, the number. of convolution layers, and the number of strides. Simulation has been done using three publicly available datasets, and the accuracy obtained is 87.8%, 92.3%, and 88.7%, respectively, which outperforms other models. No other state-of-the-art model has used DE for hyper-parameter tuning in CNN models. Also, no other additional segmentation approach or handcrafted features have been used. The model has been kept simple to reduce computational costs.
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增强糖尿病眼病检测能力:利用差分进化优化卷积神经网络
糖尿病眼检测已成为全球关注的主要问题,使用深度卷积神经网络(DCNN)进行自动检测可有效解决这一问题。与其他先进模型相比,卷积神经网络模型具有更好的检测和分类准确性。本文针对糖尿病视网膜病变(DR)和青光眼图像的单步分类提出了微分进化(DE)优化 CNN。微分进化论用于找出 CNN 四个超参数的优化值,即第一层的滤波器数量、滤波器大小、卷积层数和步长数。利用三个公开的数据集进行了仿真,得到的准确率分别为 87.8%、92.3% 和 88.7%,优于其他模型。其他最先进的模型都没有在 CNN 模型中使用 DE 进行超参数调整。此外,也没有使用其他额外的分割方法或手工特征。为了降低计算成本,该模型一直保持简单。
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