Keywords
Deep learning
Convolutional neural networks
Image classification
Computed tomography (CT) images
How to Cite
Abstract
Early diagnosis of lung cancer is critical for improving patient prognosis. While Computer-Aided Diagnosis (CAD) systems leveraging deep learning have shown promise, the selection of an optimal model architecture remains a key challenge. This study presents a comparative analysis of three prominent Convolutional Neural Network (CNN) architectures InceptionV4, VGG-13, and ResNet-50 to determine their effectiveness in classifying lung cancer into benign, malignant, and normal categories from Computed Tomography (CT) images. Utilizing the publicly available IQ-OTH/NCCD dataset, a transfer learning approach was employed, where models pre-trained on ImageNet were fine-tuned for the specific classification task. To mitigate overfitting and enhance model generalization, a suite of data augmentation techniques was applied during training. It achieved an accuracy of 98.80%, with a precision of 98.97%, a recall of 96.30%, and an F1-score of 97.52%. Notably, the confusion matrix analysis revealed that InceptionV4 perfectly identified all malignant and normal cases in the test set, highlighting its clinical reliability. The study also evaluated the trade-off between diagnostic performance and computational efficiency, where InceptionV4 provided an optimal balance compared to the computationally intensive VGG-13 and the less accurate, albeit more efficient, ResNet-50. Our findings suggest that the architectural design of InceptionV4, with its multi-scale feature extraction, is exceptionally well-suited for the complexities of lung cancer diagnosis. This model stands out as a robust and highly accurate candidate for integration into clinical CAD systems, offering significant potential to assist radiologists and improve early detection outcomes.
References
Abe, A. A., M. Nyathi, A. Okunade, W. Pilloy, B. Kgole, et al., 2025. A robust deep learning algorithm for lung cancer detection from computed tomography images. Intelligence-Based Medicine, 11: 100203.
Ahmed, I., A. Chehri, G. Jeon, and F. Piccialli, 2022. Automated pulmonary nodule classification and detection using deep learning architectures. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 20: 2445–2456.
Bagheri Tofighi, A., A. Ahmadi, and H. Mosadegh, 2025. Improving lung cancer detection via MobileNetV2 and Stacked-GRU with explainable AI. International Journal of Information Technology, 17: 1189–1196.
Bayram, B., I. Kunduracioglu, S. Ince, and I. Pacal, 2025. A systematic review of deep learning in MRI-based cerebral vascular occlusion-based brain diseases. Neuroscience.
Blandin Knight, S., P. A. Crosbie, H. Balata, J. Chudziak, T. Hussell, et al., 2017. Progress and prospects of early detection in lung cancer. Open Biology, 7: 170070.
Cakmak, Y. and I. Pacal, 2025. Enhancing breast cancer diagnosis: A comparative evaluation of machine learning algorithms using the Wisconsin dataset. Journal of Operations Intelligence, 3: 175–196.
Cakmak, Y., S. Safak, M. A. Bayram, and I. Pacal, 2024. Comprehensive evaluation of machine learning and ANN models for breast cancer detection. Computer and Decision Making: An International Journal, 1: 84–102.
Coşkun, D., D. Karaboğa, A. Baştürk, B. Akay, Ö. U. Nalbantoğlu, et al., 2023. A comparative study of YOLO models and a transformer-based YOLOv5 model for mass detection in mammograms. Turkish Journal of Electrical Engineering and Computer Sciences, 31: 1294–1313.
Deepajothi, S., R. Balaji, T. Madhuvanthi, P. Priakanth, T. Daniya, et al., 2022. Detection and stage classification of U-Net segmented lung nodules using CNN. In 2022 5th International Conference on Multimedia, Signal Processing and Communication Technologies (IMPACT), pp. 1–5, IEEE.
Fontana, R. S., D. R. Sanderson, W. F. Taylor, L. B. Woolner, W. E. Miller, et al., 1984. Early lung cancer detection: Results of the initial (prevalence) radiologic and cytologic screening in the Mayo Clinic study. American Review of Respiratory Disease, 130: 561–565.
He, K., X. Zhang, S. Ren, and J. Sun, 2016. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778.
Inage, T., T. Nakajima, I. Yoshino, and K. Yasufuku, 2018. Early lung cancer detection. Clinics in Chest Medicine, 39: 45–55.
İnce, S., I. Kunduracioglu, B. Bayram, and I. Pacal, 2025. U-Net-based models for precise brain stroke segmentation. Chaos Theory and Applications, 7: 50–60.
Işık, G. and İ. Paçal, 2024. Few-shot classification of ultrasound breast cancer images using meta-learning algorithms. Neural Computing and Applications, 36: 12047–12059.
Karaman, A., D. Karaboga, I. Pacal, B. Akay, A. Basturk, et al., 2023a. Hyper-parameter optimization of deep learning architectures using Artificial Bee Colony (ABC) algorithm for high performance real-time automatic colorectal cancer (CRC) polyp detection. Applied Intelligence, 53: 15603–15620.
Karaman, A., I. Pacal, A. Basturk, B. Akay, U. Nalbantoglu, et al., 2023b. Robust real-time polyp detection system design based on YOLO algorithms by optimizing activation functions and hyperparameters with Artificial Bee Colony (ABC). Expert Systems with Applications, 221: 119741.
Kareem, H. F., M. S. Al-Husieny, F. Y. Mohsen, E. A. Khalil, and Z. S. Hassan, 2021. Evaluation of SVM performance in the detection of lung cancer in marked CT scan dataset. Indonesian Journal of Electrical Engineering and Computer Science, 21: 1731.
Kavitha, S., E. Patnala, H. R. Sangaraju, R. Bingu, S. Adinarayana, et al., 2025. An optimized multi-head attention based fused depthwise convolutional model for lung cancer detection. Expert Systems with Applications, 271: 126596.
Kayadibi, I. and G. E. Güraksın, 2023. An early retinal disease diagnosis system using OCT images via CNN-based stacking ensemble learning. International Journal for Multiscale Computational Engineering, 21.
Kayadibi, I., G. E. Güraksın, and U. Köse, 2023. A hybrid R-FTCNN based on principal component analysis for retinal disease detection from OCT images. Expert Systems with Applications, 230: 120617.
Krizhevsky, A., I. Sutskever, and G. E. Hinton, 2017. ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60: 84–90.
Kumar, A., M. K. Ansari, and K. K. Singh, 2025. A combined approach of vision transformer and transfer learning based model for accurate lung cancer classification. SN Computer Science, 6: 509.
Kumar, V. and B. Bakariya, 2021. Classification of malignant lung cancer using deep learning. Journal of Medical Engineering & Technology, 45: 85–93.
Kurtulus, I. L., M. Lubbad, O. M. D. Yilmaz, K. Kilic, D. Karaboga, et al., 2024. A robust deep learning model for the classification of dental implant brands. Journal of Stomatology, Oral and Maxillofacial Surgery, 125: 101818.
LeCun, Y., Y. Bengio, and G. Hinton, 2015. Deep learning. Nature, 521: 436–444.
Li, G., W. Zhou, W. Chen, F. Sun, Y. Fu, et al., 2020. Study on the detection of pulmonary nodules in CT images based on deep learning. IEEE Access, 8: 67300–67309.
Liu, W., Z. Wang, X. Liu, N. Zeng, Y. Liu, et al., 2017. A survey of deep neural network architectures and their applications. Neurocomputing, 234: 11–26.
Lubbad, M., D. Karaboga, A. Basturk, B. Akay, U. Nalbantoglu, et al., 2024a. Machine learning applications in detection and diagnosis of urology cancers: A systematic literature review. Neural Computing and Applications, 36: 6355–6379.
Lubbad, M. A., I. L. Kurtulus, D. Karaboga, K. Kilic, A. Basturk, et al., 2024b. A comparative analysis of deep learning-based approaches for classifying dental implants decision support system. Journal of Imaging Informatics in Medicine, 37: 2559–2580.
Ma, J., R. P. Sheridan, A. Liaw, G. E. Dahl, and V. Svetnik, 2015. Deep neural nets as a method for quantitative structure–activity relationships. Journal of Chemical Information and Modeling, 55: 263–274.
Mikolov, T., A. Deoras, D. Povey, L. Burget, and J. Černocký, 2011. Strategies for training large scale neural network language models. In 2011 IEEE Workshop on Automatic Speech Recognition & Understanding, pp. 196–201, IEEE.
Mohanapriya, N., B. Kalaavathi, and T. Senthil Kumar, 2019. Lung tumor classification and detection from CT scan images using deep convolutional neural networks (DCNN). In 2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), pp. 800–805, IEEE.
Monkam, P., S. Qi, H. Ma, W. Gao, Y. Yao, et al., 2019. Detection and classification of pulmonary nodules using convolutional neural networks: A survey. IEEE Access, 7: 78075–78091.
Mumuni, A., F. Mumuni, and N. K. Gerrar, 2024. A survey of synthetic data augmentation methods in machine vision. Machine Intelligence Research, 21: 831–869.
Musthafa, M. M., I. Manimozhi, T. Mahesh, and S. Guluwadi, 2024. Optimizing double-layered convolutional neural networks for efficient lung cancer classification through hyperparameter optimization and advanced image pre-processing techniques. BMC Medical Informatics and Decision Making, 24: 142.
Ozdemir, B., E. Aslan, and I. Pacal, 2025. Attention enhanced InceptionNext-based hybrid deep learning model for lung cancer detection. IEEE Access.
Ozdemir, O., R. L. Russell, and A. A. Berlin, 2019. A 3D probabilistic deep learning system for detection and diagnosis of lung cancer using low-dose CT scans. IEEE Transactions on Medical Imaging, 39: 1419–1429.
Pacal, İ., 2022. Deep learning approaches for classification of breast cancer in ultrasound (US) images. Journal of the Institute of Science and Technology, 12: 1917–1927.
Pacal, I., 2024. MaxCervixT: A novel lightweight Vision Transformer-based approach for precise cervical cancer detection. Knowledge-Based Systems, 289: 111482.
Pacal, İ., 2025. Diagnostic analysis of various cancer types with artificial intelligence.
Pacal, I., O. Akhan, R. T. Deveci, and M. Deveci, 2025. NextBrain: Combining local and global feature learning for brain tumor classification. Brain Research, p. 149762.
Pacal, I. and O. Attallah, 2025. InceptionNext-Transformer: A novel multi-scale deep feature learning architecture for multimodal breast cancer diagnosis. Biomedical Signal Processing and Control, 110: 108116.
Pacal, I. and S. Kılıcarslan, 2023. Deep learning-based approaches for robust classification of cervical cancer. Neural Computing and Applications, 35: 18813–18828.
Puttagunta, M. and S. Ravi, 2021. Medical image analysis based on deep learning approach. Multimedia Tools and Applications, 80: 24365–24398.
Salama, W. M., A. Shokry, and M. H. Aly, 2022. A generalized framework for lung cancer classification based on deep generative models. Multimedia Tools and Applications, 81: 32705–32722.
Sharif, M. I., M. A. Khan, M. Alhussein, K. Aurangzeb, and M. Raza, 2022. A decision support system for multimodal brain tumor classification using deep learning. Complex & Intelligent Systems, 8: 3007–3020.
Siegel, R. L., A. N. Giaquinto, and A. Jemal, 2024. Cancer statistics, 2024. CA: A Cancer Journal for Clinicians, 74.
Simonyan, K. and A. Zisserman, 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Szegedy, C., V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, 2016. Rethinking the Inception architecture for computer vision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826.
Taheri, F. and K. Rahbar, 2025. GoogLeNet’s semantic hierarchical feature fusion for the classification of lung cancer CT images. Neural Computing and Applications, pp. 1–21.
Wang, Z., P. Wang, K. Liu, P. Wang, Y. Fu, et al., 2024. A comprehensive survey on data augmentation. arXiv preprint arXiv:2405.09591.
Zeynalov, J., Y. Çakmak, and İ. Paçal, 2025. Automated apple leaf disease classification using deep convolutional neural networks: A comparative study on the Plant Village dataset. Journal of Computer Science and Digital Technologies, 1: 5–17.
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