Varadharajalu Krishnamoorthy, Shanmugam Logeswari
Department of Computer Science and Engineering, Sri Venkateswara College of Engineering, Chennai, India.
Department of Information Technology, Karpagam College of Engineering, Coimbatore, India.
Network. 2025 Mar 27:1-55. doi: 10.1080/0954898X.2025.2481958.
Glaucoma is a leading cause of blindness, requiring early detection for effective management. Traditional diagnostic methods have challenges such as precise segmentation of small structures and accurate classification of disease stages remain. This research addresses these challenges by developing an optimized hybrid classification model for automated glaucoma diagnosis. At first, the preprocessing stage employs the histogram equalization technique known as Contrast Limited Adaptive Histogram Equalization (CLAHE) technique. Consequently, an improved U-Net segmentation process implemented with the proposed cross-entropy loss function is utilized. Then, features such as fractal features, cup-to-disc-based features, Inferior-Superior-Nasal-Temporal (ISNT) rule-based features and improved Pyramid Histogram of Orient Gradient (PHOG) based features are extracted. Further, a hybrid classification model, a combination of Improved Convolutional Neural Network (ICNN) and optimized Recurrent Neural Network (RNN) classifiers for diagnosing glaucoma disease. Also, to improve the performance of the diagnosis process, a new Opposition-based Learning-enabled Namib Beetle Optimization (OBL-NBO) approach is proposed to optimize the weights of the RNN classifier. Moreover, the ICNN classifier is employed for classifying the presence of glaucoma and non-glaucoma conditions. The proposed OBL-NBO scheme achieved an accuracy of 0.927 for dataset 1 and 0.945 for dataset 2 at an 80% training data.
青光眼是导致失明的主要原因之一，需要早期检测以便进行有效管理。传统诊断方法存在一些挑战，例如小结构的精确分割以及疾病阶段的准确分类仍然存在问题。本研究通过开发一种用于青光眼自动诊断的优化混合分类模型来应对这些挑战。首先，预处理阶段采用一种名为对比度受限自适应直方图均衡化（CLAHE）的直方图均衡化技术。随后，利用所提出的交叉熵损失函数实现了改进的U-Net分割过程。然后，提取诸如分形特征、基于杯盘比的特征、基于鼻颞上下（ISNT）规则的特征以及改进的基于方向梯度金字塔直方图（PHOG）的特征等特征。此外，构建了一种混合分类模型，它是用于诊断青光眼疾病的改进卷积神经网络（ICNN）和优化循环神经网络（RNN）分类器的组合。而且，为了提高诊断过程的性能，提出了一种新的基于对立学习的纳米布甲虫优化（OBL-NBO）方法来优化RNN分类器的权重。此外，使用ICNN分类器对青光眼和非青光眼情况进行分类。所提出的OBL-NBO方案在80%的训练数据下，数据集1的准确率达到0.927，数据集2的准确率达到0.945。
