Robust artificial intelligence frameworks for lung cancer subtyping and malignancy detection on thoracic CT.
Authors
Affiliations (6)
Affiliations (6)
- Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia.
- Center for Research Excellence in Artificial Intelligence and Data Science (AIADS), King Abdulaziz University, Jeddah, Saudi Arabia.
- Department of Computer Engineering, Faculty of Engineering, Bingöl University, Bingöl, Türkiye, Turkey. [email protected].
- Department of Computer Engineering, Faculty of Engineering, Igdir University, Igdir, 76000, Türkiye.
- Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Fenerbahce University, Istanbul, Türkiye, Turkey.
- Department of Electronics and Information Technologies, Faculty of Architecture and Engineering, Nakhchivan State University, Nakhchivan, 7012, AZ, Azerbaijan.
Abstract
Accurate characterization of thoracic malignancies on computed tomography (CT) remains challenging because histological subtype differentiation and nodule malignancy assessment require different visual cues, ranging from localized structural abnormalities to broader contextual patterns. This study presents a controlled comparative evaluation of convolutional neural network (CNN) and Vision Transformer (ViT) architectures for two CT-based thoracic oncology tasks: four-class histological subtyping and three-class lung cancer classification. A total of 30 contemporary architectures, including 15 CNN-based and 15 ViT-based models, were evaluated under a unified experimental setting using two publicly available datasets. The Chest CT-Scan Images dataset was used for Adenocarcinoma, Large cell carcinoma, Squamous cell carcinoma, and normal classification, while the IQ-OTH/NCCD dataset was used for normal, benign, and malignant classification. Model performance was assessed using accuracy, precision, recall, F1-score, AUC, parameter count, confusion matrices, and Grad-CAM-based visual interpretation. For histological subtyping, DeiT3-Base achieved the strongest performance among the evaluated models, with an F1-score of 0.9732, followed closely by high-performing CNN models such as InceptionNeXt-Base. For lung cancer classification, EfficientNetV2-Small, Swin-Base, and MViTv2-Base achieved the highest F1-score of 0.9816. These findings indicate that attention-based architectures may be advantageous for subtype discrimination requiring broader contextual modeling, whereas efficient CNNs and hierarchical transformers remain highly competitive for localized structural abnormalities. Grad-CAM visualizations provided qualitative evidence that high-performing models attended to relevant lesion and parenchymal regions.