Optimizing Lung Cancer Detection in CT Imaging: A Wavelet Multi-Layer Perceptron (WMLP) Approach Enhanced by Dragonfly Algorithm (DA)

Early-stage detection is critical, as it significantly improves the five-year survival rate from a dismal 5% in late-stage diagnoses to over 50% [2]. The advent of advanced screening technologies promises to substantially improve patient prognoses. The field of medical imaging has been revolutionized by recent strides in deep learning, yielding significant enhancements in the detection and classification of lung cancer from CT images. Innovations such as the 3D Convolutional Neural Network (CNN) approach by Diviya et al. (2024) and the LCD-Capsule Network by Bushara et al. (2023) have demonstrated the potential of these models to transform early detection and diagnosis [3, 4]. X-ray and computed tomography (CT) scans are pivotal in lung cancer diagnostics, offering high-resolution imagery that outperforms traditional radiography in detecting small and low-contrast pulmonary nodules [5, 6, 7].