Analysis of Variance Application in the Construction of Classifier Ensemble Based on Optimal Feature Subset for the Task of Supporting Glaucoma Diagnosis

Sułot, Dominika; Zyblewski, Paweł; Ksieniewicz, Paweł

doi:10.1007/978-3-030-77967-2_10

Cited by 1 publication

(1 citation statement)

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“…Feature selection (FS) techniques are pivotal in improving dataset quality, decreasing dimensionality, reducing training time, and mitigating overfitting. Various FS techniques have been proposed in the task of glaucoma classification [ 13 , 14 , 15 , 16 , 17 ]. Despite the availability of various FS techniques, metaheuristic algorithms (MAs) have gained traction for their adaptability and efficacy in optimizing FS processes.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Medical Image Classification with an Advanced Feature Selection Algorithm: A Novel Approach to Improving the Cuckoo Search Algorithm by Incorporating Caputo Fractional Order

Habeb,

Taresh,

et al. 2024

Diagnostics

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Glaucoma is a chronic eye condition that seriously impairs vision and requires early diagnosis and treatment. Automated detection techniques are essential for obtaining a timely diagnosis. In this paper, we propose a novel method for feature selection that integrates the cuckoo search algorithm with Caputo fractional order (CFO-CS) to enhance the performance of glaucoma classification. However, when using the infinite series, the Caputo definition has memory length truncation issues. Therefore, we suggest a fixed memory step and an adjustable term count for optimization. We conducted experiments integrating various feature extraction techniques, including histograms of oriented gradients (HOGs), local binary patterns (LBPs), and deep features from MobileNet and VGG19, to create a unified vector. We evaluate the informative features selected from the proposed method using the k-nearest neighbor. Furthermore, we use data augmentation to enhance the diversity and quantity of the training set. The proposed method enhances convergence speed and the attainment of optimal solutions during training. The results demonstrate superior performance on the test set, achieving 92.62% accuracy, 94.70% precision, 93.52% F1-Score, 92.98% specificity, 92.36% sensitivity, and 85.00% Matthew’s correlation coefficient. The results confirm the efficiency of the proposed method, rendering it a generalizable and applicable technique in ophthalmology.

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