# Hybrid CNN with Chebyshev Polynomial Expansion for Medical Image Analysis

arXiv: https://arxiv.org/abs/2504.06811 · HTML: https://mlsanity.com/paper/2504.06811/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 9 Apr 2025.

## Builds on (most cited first, 5 of 5)

- [CNN-based Segmentation of Medical Imaging Data](https://mlsanity.com/paper/1701.03056/) (2017)
- [DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification](https://mlsanity.com/paper/1801.09555/) (2018)
- [A Deep Convolutional Neural Network for Lung Cancer Diagnostic](https://mlsanity.com/paper/1804.08170/) (2018)
- [Single-view 2D CNNs with Fully Automatic Non-nodule Categorization for False Positive Reduction in Pulmonary Nodule Detection](https://mlsanity.com/paper/2003.04454/) (2020)
- [ProKAN: Progressive Stacking of Kolmogorov-Arnold Networks for Efficient Liver Segmentation](https://mlsanity.com/paper/2412.19713/) (2024)

## Abstract (arXiv)

Lung cancer remains one of the leading causes of cancer-related mortality worldwide, with early and accurate diagnosis playing a pivotal role in improving patient outcomes. Automated detection of pulmonary nodules in computed tomography (CT) scans is a challenging task due to variability in nodule size, shape, texture, and location. Traditional Convolutional Neural Networks (CNNs) have shown considerable promise in medical image analysis; however, their limited ability to capture fine-grained spatial-spectral variations restricts their performance in complex diagnostic scenarios. In this study, we propose a novel hybrid deep learning architecture that incorporates Chebyshev polynomial expansions into CNN layers to enhance expressive power and improve the representation of underlying anatomical structures. The proposed Chebyshev-CNN leverages the orthogonality and recursive properties of Chebyshev polynomials to extract high-frequency features and approximate complex nonlinear functions with greater fidelity. The model is trained and evaluated on benchmark lung cancer imaging datasets, including LUNA16 and LIDC-IDRI, achieving superior performance in classifying pulmonary nodules as benign or malignant. Quantitative results demonstrate significant improvements in accuracy, sensitivity, and specificity compared to traditional CNN-based approaches. This integration of polynomial-based spectral approximation within deep learning provides a robust framework for enhancing automated medical diagnostics and holds potential for broader applications in clinical decision support systems.