Title: Hybrid CNN with Chebyshev polynomial expansion for medical image analysis

Authors: Abhinav Roy; Bhavesh Gyanchandani; Aditya Oza

Addresses: Department of DSAI, IIIT, Naya Raipur, Raipur, India ' Department of DSAI, IIIT, Naya Raipur, Raipur, India ' Department of DSAI, IIIT, Naya Raipur, Raipur, India

Abstract: Lung cancer remains a leading cause of cancer-related deaths, where early and accurate diagnosis is vital. Automated detection of pulmonary nodules in CT scans is challenging due to variations in nodule characteristics. While CNNs have shown promise, they struggle to capture fine-grained spatial-spectral features. We propose a hybrid CNN architecture enhanced with Chebyshev polynomial expansions, leveraging their orthogonality and approximation properties to extract high-frequency features and improve nonlinear function modelling. Evaluated on LUNA16 and LIDC-IDRI datasets, our model outperforms standard CNNs in classifying nodules as benign or malignant, achieving notable gains in accuracy, sensitivity, and specificity. This method offers a robust framework for medical image analysis and clinical decision support.

Keywords: Chebyshev polynomials; convolutional neural networks; CNNs; deep learning; function approximation; hybrid deep learning model.

DOI: 10.1504/IJCAST.2026.155953

International Journal of Complexity in Applied Science and Technology, 2026 Vol.2 No.3, pp.213 - 227

Received: 25 May 2025
Accepted: 29 Aug 2025
Published online: 27 Aug 2026 *

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