@article {10.3844/jcssp.2026.1785.1796,
article_type = {journal},
title = {Hybrid CNN-Based Transformer Pipeline With Radiomic Fusion for Multi-Class Lung Cancer Detection},
author = {Vij, Aanchal and Kaswan, Kuldeep Singh and Nayyar, Anand},
volume = {22},
number = {6},
year = {2026},
month = {Jun},
pages = {1785-1796},
doi = {10.3844/jcssp.2026.1785.1796},
url = {https://thescipub.com/abstract/jcssp.2026.1785.1796},
abstract = {Early detection of lung cancer remains challenging due to high intra-class variation and inter-class similarity in Computed Tomography (CT) images. In this paper, we propose a hybrid deep learning model that combines convolutional, attention-based, and transformer-guided representations to address these challenges in multi-class lung cancer classification. For deep feature extraction, we use the EfficientNetV2-S architecture augmented with a Convolutional Block Attention Module to emphasize salient spatial and channel information. A transformer encoder captures global contextual dependencies, and texture-based radiomic features are incorporated to further enrich the representation. The resulting features are fused into a single embedding, which is then classified as normal, benign, or malignant. Experiments on the IQ-OTHNCCD dataset demonstrate that the proposed framework achieves superior performance across multiple metrics, accuracy, recall, precision, F1 score, and AUC, and outperforms state-of-the-art methods.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}