Khan, Shat-El-ShahriarAdor, Khalid HasanAlvee, Hasin MahtabRaji, Hamim Saad AlAlam, Syed Md. ShadmanMollah, Md. Farhad2026-08-102026-08-102026-01-01S. -E. -S. Khan, K. H. Ador, H. M. Alvee, H. S. A. Raji, S. M. S. Alam and M. F. Mollah, "Evaluating CoAtNet for Multiclass Lung Cancer Classification on CT Images: A Benchmark Study on the IQ-OTH/NCCD Dataset," 2026 IEEE 2nd International Conference on Quantum Photonics, Artificial Intelligence & Networking (QPAIN), Chittagong, Bangladesh, 2026, pp. 1-6, doi: 10.1109/QPAIN69676.2026.11545570.97983315499092-s2.0-105042828502https://hdl.handle.net/10361/28892Accurate classification of lung cancer from Computed Tomography (CT) scans remains a significant challenge in medical image analysis. In this research, we introduce an incremental deep learning framework that leverages CoAtNet's hybrid convolution-attention architecture for optimal multiclass lung cancer classification. Our approach integrates extensive preprocessing, including class-balancing via selective augmentation, high-resolution resizing, and adaptive color-space transformations to mitigate dataset imbalance and improve generalizability. We fine-tuned a pretrained CoAtNet-0-RW-224 model on a selected subset of the IQ-OTH / NCCD data set, obtaining an overall classification accuracy of 98.17% and weighted precision of 97.81 %, recall of 97.77 %, and F1 score of 97.12 %. A comparative study through a confusion matrix and classification report confirms competitive performance in all three diagnostic categories: benign, malignant, and normal. Experimental results establish CoAtNet as a robust architecture for CT-based lung cancer screening and provide a compelling baseline for future research utilizing transformer-convolutional hybrid models in medical imaging applications.6 pagesen-USfalseAttention mechanismCNNCoAtNetDeep learningEnsemble learningIQ-OTH/NCCD datasetLung cancer classificationMedical image analysisVision transformerLungs--Cancer.Diagnostic imaging.Machine learning.Attention--Computer simulation.Conference Proceeding10.1109/QPAIN69676.2026.11545570