The Augmented IQ-OTHNCCD Lung Cancer Dataset

This repository contains the minimal reproducibility dataset supporting the research work titled:"Contrastive Attention Learning Network Based Hybrid Model for Lung Cancer Detection in CT Images" The study focuses on automated lung cancer classification from computed tomography (CT) images using deep learning and feature fusion techniques. The original CT images were obtained from the publicly available IQ-OTHNCCD Lung Cancer Dataset. In accordance with repository and journal policies, raw CT images are not redistributed in this repository. Instead, this repository provides the essential materials required to reproduce the findings reported in the manuscript, including: • Patient-wise training and testing split indices • Detailed preprocessing pipeline description • Extracted deep learning feature representations • Extracted radiomic feature representations • Corresponding class labels The patient-wise split strategy ensures the prevention of data leakage and supports reliable model evaluation. The materials provided here enable transparent and reproducible validation of the proposed methodology while respecting data ownership and ethical considerations. Researchers can reproduce the results by downloading the original IQ-OTHNCCD dataset from its official source and applying the split indices and preprocessing steps described in this repository.