A Self-Operational Convolutional Neural Networks With Convergent Cross-Mapping and Its Application in Parkinson's Disease Classification

• Published in: IEEE access 2024, Vol.12, p.83140-83153
• Main Authors: Sekaran, Kaushik, Harshavardhan, A., Sandhya, N., Sudha, C., Nagaraju, Gujjeti, Bukya, Hanumanthu, Sahay, Rashmi, Kalaivani, J.
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Classification; convergent cross-mapping; Convolutional neural networks; Decision trees; Degenerative diseases; Diseases; Feature extraction; health controls; Information transfer; Machine learning; Mapping; Medical diagnostic imaging; Neural engineering; Neural networks; Parameter modification; Parkinson's disease; self-operating convolutional neural network; Training
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3412808

Parkinson's disease (PD) is a progressive neurodegenerative disease with multiple motor and non-motor characteristics. PD patients commonly face vocal impairments during the early stages of the disease. Therefore, diagnosis systems based on vocal disorders are at the forefront of recent PD detection studies. Our study proposes two frameworks based on Convolutional Neural Networks to classify Parkinson's disease (PD). In recent years, Convolutional Neural Networks (CNNs) have proven highly effective in various medical applications, particularly disease classification. However, standard CNN designs have significant limitations because they require extensive manual calibration and supervision, which can result in biases and poor performance in practical applications. This paper proposes the Self-Operating Convolutional Neural Network (SOCNN) in conjunction with Convergent Cross-Mapping (CCM) to address these issues. The SOCNN architecture is intended to modify its internal parameters automatically, eliminating the need for manual intervention during training and increasing the model's adaptability to unknown data. Adopting CCM principles, we construct a seamless connection between the input and output domains, allowing for rapid information transfer and preservation, which are crucial for accurate disease classification. To this end, we construct causal networks, extract network features, and perform deep learning analysis to distinguish Parkinson's disease patients (PD) from age and gender-matched healthy controls (HC). Using a large dataset of Parkinson's Disease (PD) patients and healthy controls, the effectiveness of the proposed SOCNN with CCM is evaluated. Specifically, we use the SOCNN-CCM to compute the centrality of the network nodes, which act as features for the classification models. Extensive experiments are conducted to compare the SOCNN to conventional CNN models and innovative techniques. The results demonstrate that the SOCNN-CMM outperforms state-of-the-art in terms of accuracy, sensitivity, and specificity when classifying Parkinson's patients, confirming its diagnostic potential.