Department of Computer Science and Engineering, C.V. Raman Global University, Bhubaneswar, 751012, India.
Department of Computer Science, Birla Institute of Technology, Ranchi, Jharkhand, 847226, India.
Sci Rep. 2024 Nov 29;14(1):29660. doi: 10.1038/s41598-024-79710-7.
Glaucoma is defined as progressive optic neuropathy that damages the structural appearance of the optic nerve head and is characterized by permanent blindness. For mass fundus image-based glaucoma classification, an improved automated computer-aided diagnosis (CAD) model performing binary classification (glaucoma or healthy), allowing ophthalmologists to detect glaucoma disease correctly in less computational time. We proposed learning technique called fast discrete curvelet transform with wrapping (FDCT-WRP) to create feature set. This method is entitled extracting curve-like features and creating a feature set. The combined feature reduction techniques named as principal component analysis and linear discriminant analysis, have been applied to generate prominent features and decrease the feature vector dimension. Lastly, a newly improved learning algorithm encompasses a modified pelican optimization algorithm (MOD-POA) and an extreme learning machine (ELM) for classification tasks. In this MOD-POA+ELM algorithm, the modified pelican optimization algorithm (MOD-POA) has been utilized to optimize the parameters of ELM's hidden neurons. The effectiveness has been evaluated using two standard datasets called G1020 and ORIGA with the [Formula: see text]-fold stratified cross-validation technique to ensure reliable evaluation. Our employed scheme achieved the best results for both datasets obtaining accuracy of 93.25% (G1020 dataset) and 96.75% (ORIGA dataset), respectively. Furthermore, we have utilized seven Explainable AI methodologies: Vanilla Gradients (VG), Guided Backpropagation (GBP ), Integrated Gradients ( IG), Guided Integrated Gradients (GIG), SmoothGrad, Gradient-weighted Class Activation Mapping (GCAM), and Guided Grad-CAM (GGCAM) for interpretability examination, aiding in the advancement of dependable and credible automation of healthcare detection of glaucoma.
青光眼被定义为一种进行性视神经病变，损害视神经头部的结构外观，并以永久性失明为特征。为了进行大规模眼底图像的青光眼分类，我们提出了一种改进的自动化计算机辅助诊断 (CAD) 模型，用于执行二分类（青光眼或健康），使眼科医生能够在更短的计算时间内正确检测出青光眼疾病。我们提出了一种名为快速离散曲线变换与缠绕（FDCT-WRP）的学习技术来创建特征集。该方法旨在提取曲线特征并创建特征集。组合特征降维技术，如主成分分析和线性判别分析，已被应用于生成突出特征和减少特征向量维度。最后，一个新的改进学习算法包含一个改进的鹈鹕优化算法（MOD-POA）和一个极限学习机（ELM）用于分类任务。在这个 MOD-POA+ELM 算法中，改进的鹈鹕优化算法（MOD-POA）已被用于优化 ELM 的隐藏神经元的参数。使用两种标准数据集 G1020 和 ORIGA 并采用 [Formula: see text]-折分层交叉验证技术进行了有效性评估，以确保可靠的评估。我们的方案在两个数据集上都取得了最佳结果，分别获得了 93.25%（G1020 数据集）和 96.75%（ORIGA 数据集）的准确率。此外，我们还利用了七种可解释人工智能方法：Vanilla Gradients (VG)、Guided Backpropagation (GBP)、Integrated Gradients (IG)、Guided Integrated Gradients (GIG)、SmoothGrad、Gradient-weighted Class Activation Mapping (GCAM)和 Guided Grad-CAM (GGCAM)进行可解释性检查，有助于推进可靠和可信的医疗保健检测青光眼的自动化。
