基于CNN架构的高效青光眼筛查技术

J. Srinivasa, S. Deekshitha, U. Sushil, N. Dhiya, N. Kumar
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引用次数: 2

摘要

青光眼是世界上第二种不可逆转的眼病,如果不及早治疗,可能导致失明。在早期阶段,由于没有任何症状,检测这种疾病非常繁琐。因此,我们建立了一种卷积神经网络的监督方法,以尽早发现疾病。在我们的模型中,使用了705张图像的ACRIMA数据集,其中396张是青光眼图像,309张是非青光眼图像。完整的数据集分为80%用于训练,20%用于测试。在给CNN模型之前对数据集进行预处理。我们的CNN模型由8层组成,其中4层卷积层用于特征提取,4层是完全连接层,用于青光眼和非青光眼图像的分类。在学习率为0.0001和epoch为100时,获得了最好的性能。本文通过绘制混淆矩阵、灵敏度、特异度、准确度、精密度(PRC)统计指标和AUC为0.99来评价青光眼-深度系统的性能
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A High Performance Glaucoma Screening Technique Using CNN Architecture
Glaucoma is a second irreversible eye disease across the world that can lead to blindness, if not treated early. At an early stage, it is very tedious to detect the disease as it does not show any symptoms. So, we build a super-vised method for convolutional neural network to detect the disease as early as possible. In our model ACRIMA data set of 705 images are used where 396 are glaucomatous images and 309 are non-glaucomatous images. The complete data set is divided into 80% for training and 20% for testing. The data set is preprocessed before giving to the CNN model. Our CNN model consists of 8 layers where, 4 convolutional layers for feature extraction and 4 is fully connected layer to classify between glaucomatous and non-glaucomatous imag-es. The best performance is obtained at the learning rate and epochs of 0.0001 and 100.In this work we evaluated the performance of Glaucoma-Deep system by plotting confusion matrix, the sensitivity, specificity, accuracy, precision (PRC) statistical measures and AUC of0.99
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