AI driven hybrid convolutional and transformer based deep learning architecture for precise lung nodule classification.

Abstract

Computed tomography imaging enables early lung cancer screening yet subtle pulmonary nodules often evade manual review. This study aims to design and validate a comprehensive image enhancement and segmentation pipeline that detects nodules with high spatial accuracy while maintaining low false positive rates. The publicly available Lung Image Database Consortium Image Database Resource Initiative (LIDC IDRI) collection provided a diverse validation environment. The proposed framework first increases local contrast through adaptive stretching, then preserves edges with anisotropic diffusion, selects seed points through adaptive thresholding, expands regions with three-dimensional connectivity, and refines boundaries using morphological operations. Experiments quantified performance against reference masks on one thousand scans. The method achieved a mean overlap score of 0.83, a sensitivity of 0.92, and an average of 1.5 false positives per scan, outperforming threshold and watershed baselines. These findings show that meticulous feature enhancement coupled with shape-based refinement can deliver reproducible and clinically meaningful support for radiologists during routine screening.