Open Access

ARTICLE

Hybrid Techniques of Multi-CNN and Ensemble Learning to Analyze Handwritten Spiral and Wave Drawing for Diagnosing Parkinson’s Disease

Mohammed Al-Jabbar¹, Mohammed Alshahrani^1,*, Ebrahim Mohammed Senan^2,3, Ibrahim Abunadi⁴, Sultan Ahmed Almalki¹, Eman A Alshari^3,5

1 Computer Department, Applied College, Najran University, Najran, 66462, Saudi Arabia
2 Department of Computer Science, College of Applied Sciences, Hajjah University, Hajjah, 9671, Yemen
3 Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Al-Razi University, Sana’a, 9677, Yemen
4 Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia
5 Computer Science and Information Technology Department, Thamar University, Dhamar, 9676, Yemen

* Corresponding Author: Mohammed Alshahrani. Email:

(This article belongs to the Special Issue: Advanced Computational Intelligence Techniques, Uncertain Knowledge Processing and Multi-Attribute Group Decision-Making Methods Applied in Modeling of Medical Diagnosis and Prognosis)

Computer Modeling in Engineering & Sciences 2025, 143(2), 2429-2457. https://doi.org/10.32604/cmes.2025.063938

Received 29 January 2025; Accepted 15 April 2025; Issue published 30 May 2025

Abstract

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by tremors, rigidity, and decreased movement. PD poses risks to individuals’ lives and independence. Early detection of PD is essential because it allows timely intervention, which can slow disease progression and improve outcomes. Manual diagnosis of PD is problematic because it is difficult to capture the subtle patterns and changes that help diagnose PD. In addition, the subjectivity and lack of doctors compared to the number of patients constitute an obstacle to early diagnosis. Artificial intelligence (AI) techniques, especially deep and automated learning models, provide promising solutions to address deficiencies in manual diagnosis. This study develops robust systems for PD diagnosis by analyzing handwritten helical and wave graphical images. Handwritten graphic images of the PD dataset are enhanced using two overlapping filters, the average filter and the Laplacian filter, to improve image quality and highlight essential features. The enhanced images are segmented to isolate regions of interest (ROIs) from the rest of the image using a gradient vector flow (GVF) algorithm, which ensures that features are extracted from only relevant regions. The segmented ROIs are fed into convolutional neural network (CNN) models, namely DenseNet169, MobileNet, and VGG16, to extract fine and deep feature maps that capture complex patterns and representations relevant to PD diagnosis. Fine and deep feature maps extracted from individual CNN models are combined into fused feature vectors for DenseNet169-MobileNet, MobileNet-VGG16, DenseNet169-VGG16, and DenseNet169-MobileNet-VGG16 models. This fusion technique aims to combine complementary and robust features from several models, which improves the extracted features. Two feature selection algorithms are considered to remove redundancy and weak correlations within the combined feature set: Ant Colony Optimization (ACO) and Maximum Entropy Score-based Selection (MESbS). These algorithms identify and retain the most strongly correlated features while eliminating redundant and weakly correlated features, thus optimizing the features to improve system performance. The fused and enhanced feature vectors are fed into two powerful classifiers, XGBoost and random forest (RF), for accurate classification and differentiation between individuals with PD and healthy controls. The proposed hybrid systems show superior performance, where the RF classifier used the combined features from the DenseNet169-MobileNet-VGG16 models with the ACO feature selection method, achieving outstanding results: area under the curve (AUC) of 99%, sensitivity of 99.6%, 99.3% accuracy, 99.35% accuracy, and 99.65% specificity.

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