e-LSTM: EfficientNet and Long Short-Term Memory Model for Detection of Glaucoma Diseases

Wiharto, Wimas Tri Harjoko, E. Suryani
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Abstract

Glaucoma is an eye disease that often has no symptoms until it is advanced. According to the World Health Organization (WHO), after cataracts, glaucoma is the second-leading cause of permanent blindness globally and is expected to affect 111.8 million patients by 2040. Early detection of glaucoma is important to reduce the risk of permanent blindness. Detection is achieved by structural measurement of early thinning of the retinal nerve fiber layer (RNFL). The RNFL is the portion of the retina located outside the optic nerve head (ONH) and can be observed in fundus images of the retina. Analysis of retinal fundus images can be performed with computer assistance using machine learning, especially deep learning. This study proposes a deep learning-based model, a convolutional neural network (CNN) using the EfficientNet architecture combined with long short-term memory (LSTM), for laucoma detection. Using ACRIMA, DRISHTI-GS, and RIM-ONE DL datasets with k-fold cross-validation, the model achieved high performance on the ACRIMA dataset: accuracy 0.9799, loss 0.0596, precision 0.9802, sensitivity 0.9799, specificity 0.9771, and F1score 0.9799. This EfficientNet and LSTM combination (e-LSTM) outperformed previous studies, offering a promising alternative for evaluating retinal fundus images in glaucoma detection.
e-LSTM:用于检测青光眼疾病的高效网络和长短期记忆模型
青光眼是一种眼科疾病,在晚期之前往往没有任何症状。根据世界卫生组织(WHO)的数据,青光眼是继白内障之后导致全球永久性失明的第二大原因,预计到 2040 年将有 1.118 亿患者受到青光眼的影响。早期发现青光眼对于降低永久性失明的风险非常重要。检测的方法是对视网膜神经纤维层(RNFL)的早期变薄进行结构测量。视网膜神经纤维层是视网膜上位于视神经头(ONH)以外的部分,可在视网膜的眼底图像中观察到。视网膜眼底图像的分析可在计算机辅助下利用机器学习,尤其是深度学习来完成。本研究提出了一种基于深度学习的模型--卷积神经网络(CNN),采用 EfficientNet 架构并结合长短期记忆(LSTM),用于检测白内障。该模型使用ACRIMA、DRISHTI-GS和RIM-ONE DL数据集进行k-fold交叉验证,在ACRIMA数据集上取得了很高的性能:准确率为0.9799,损失为0.0596,精确度为0.9802,灵敏度为0.9799,特异性为0.9771,F1score为0.9799。该 EfficientNet 和 LSTM 组合(e-LSTM)的表现优于之前的研究,为青光眼检测中的视网膜眼底图像评估提供了一种有前途的替代方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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