Deep Learning-Based Lung Cancer Diagnosis: Data Balancing, Model Optimisation and Performance Analysis

Year 2025, Volume: 12 Issue: 2, 358 - 376

Feyyaz Alpsalaz

https://doi.org/10.54287/gujsa.1648772

Abstract

Lung cancer (LC) is one of the most lethal malignancies worldwide, and early detection is essential. This study develops a deep learning (DL) based classification model for LC diagnosis using computed tomography (CT) images. Commonly used convolutional neural network (CNN) architectures, such as ResNet101, VGG19, and DenseNet121, are compared, and the model's performance is analyzed in detail. In the experiments conducted on the IQ-OTHNCCD LC dataset, the SMOTE method was applied to eliminate class imbalance, data augmentation techniques were used, and an early stopping mechanism was integrated to enhance the model's generalizability.
With an accuracy of 98%, the trial results demonstrate that the suggested ResNet101 model offers the best classification performance. Notably, the model's high accuracy in classifying malignant cases enhances its usability in clinical decision support systems. While the VGG19 model demonstrated balanced performance, the DenseNet121 model exhibited a relatively lower accuracy rate in distinguishing between benign and normal classes. Comparisons with studies in the literature indicate that the proposed model has superior accuracy and generalization capacity compared to existing methods. The results of the study show that DL based models offer a powerful alternative to automated diagnostic systems for diagnosing LC.

Keywords

Lung Cancer, Deep Learning, Computed Tomography, CNN, ResNet101

References

• AL-Huseiny, M. (2021). National Cancer Center Database (IQ-OTH/NCCD - Lung Cancer Dataset) [https://www.kaggle.com/datasets/adityamahimkar/iqothnccd-lung-cancer-dataset/data]. In Kaggle. https://doi.org/doi: 10.17632/bhmdr45bh2.2
• Aslan, E. (2025). Development of malaria diagnosis with convolutional neural network architectures: a CNN-based software for accurate cell image analysis. ITEGAM-JETIA, 11(51), 35–42. https://doi.org/10.5935/JETIA.V11I51.1392
• Asuntha, A., & Srinivasan, A. (2020). Deep learning for lung Cancer detection and classification. Multimedia Tools and Applications, 79(11–12), 7731–7762. https://doi.org/10.1007/s11042-019-08394-3
• Çelik, M., & İnik, Ö. (2023). Detection of Monkeypox Among Different Pox Diseases with Different Pre-Trained Deep Learning Models. Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 13(1), 10–21. https://doi.org/10.21597/JIST.1206453
• Crasta, L. J., Neema, R., & Pais, A. R. (2024). A novel Deep Learning architecture for lung cancer detection and diagnosis from Computed Tomography image analysis. Healthcare Analytics, 5(1) 100316. https://doi.org/10.1016/J.HEALTH.2024.100316
• Davri, A., Birbas, E., Kanavos, T., Ntritsos, G., Giannakeas, N., Tzallas, A. T., & Batistatou, A. (2023). Deep Learning for Lung Cancer Diagnosis, Prognosis and Prediction Using Histological and Cytological Images: A Systematic Review. Cancers 2023, 15(15), 3981. https://doi.org/10.3390/CANCERS15153981
• Devarajan, H. R., Balasubramanian, S., Kumar Swarnkar, S., Kumar, P., & Jallepalli, V. R. (2023). Deep Learning for Automated Detection of Lung Cancer from Medical Imaging Data. International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI 2023). https://doi.org/10.1109/ICAIIHI57871.2023.10488962
• Eren, B., Fen, Ü., Dergisi, B., Aslan, E., & Özüpak, Y. (2024). Classification of Blood Cells with Convolutional Neural Network Model. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 13(1), 314–326. https://doi.org/10.17798/BITLISFEN.1401294
• Huang, S., Arpaci, I., Al-Emran, M., Kılıçarslan, S., & Al-Sharafi, M. A. (2023). A comparative analysis of classical machine learning and deep learning techniques for predicting lung cancer survivability. Multimedia Tools and Applications, 82(22), 34183–34198. https://doi.org/10.1007/s11042-023-16349-y
• Javed, R., Abbas, T., Khan, A. H., Daud, A., Bukhari, A., & Alharbey, R. (2024). Deep learning for lungs cancer detection: a review. Artificial Intelligence Review, 57(8), 1–39. https://doi.org/10.1007/S10462-024-10807-1
• Kumar, V., Prabha, C., Sharma, P., Mittal, N., Askar, S. S., & Abouhawwash, M. (2024). Unified deep learning models for enhanced lung cancer prediction with ResNet-50–101 and EfficientNet-B3 using DICOM images. BMC Medical Imaging, 24(1), 1–21. https://doi.org/10.1186/S12880-024-01241-4
• Mamatha, B., Rashmi, D., Tiwari, K. S., Sikrant, P. A., Jovith, A. A., & Reddy, P. C. S. (2023). Lung Cancer Prediction from CT Images and using Deep Learning Techniques. 2023 2nd International Conference on Trends in Electrical, Electronics and Computer Engineering (TEECCON 2023), 263–267. https://doi.org/10.1109/TEECCON59234.2023.10335801
• Mikhael, P. G., Wohlwend, J., Yala, A., Karstens, L., Xiang, J., Takigami, A. K., Bourgouin, P. P., Chan, P., Mrah, S., Amayri, W., Juan, Y. H., Yang, C. T., Wan, Y. L., Lin, G., Sequist, L. V., Fintelmann, F. J., & Barzilay, R. (2023). Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk from a Single Low-Dose Chest Computed Tomography. Journal of Clinical Oncology, 41(12), 2191–2200. https://doi.org/10.1200/JCO.22.01345
• Mohamed, T. I. A., & Ezugwu, A. E. S. (2024). Enhancing Lung Cancer Classification and Prediction With Deep Learning and Multi-Omics Data. IEEE Access, 12(1), 59880–59892. https://doi.org/10.1109/ACCESS.2024.3394030
• Mohamed, T. I. A., Oyelade, O. N., & Ezugwu, A. E. (2023). Automatic detection and classification of lung cancer CT scans based on deep learning and ebola optimization search algorithm. PLOS ONE, 18(8), 0285796. https://doi.org/10.1371/JOURNAL.PONE.0285796
• Özdemir, B., Aslan, E., & Pacal, I. (2025). Attention Enhanced InceptionNeXt Based Hybrid Deep Learning Model for Lung Cancer Detection. IEEE Access. https://doi.org/10.1109/ACCESS.2025.3539122
• Said, Y., Alsheikhy, A. A., Shawly, T., & Lahza, H. (2023). Medical Images Segmentation for Lung Cancer Diagnosis Based on Deep Learning Architectures. Diagnostics 2023, 13(3), 546. https://doi.org/10.3390/DIAGNOSTICS13030546
• Shafi, I., Din, S., Khan, A., Díez, I. D. L. T., Casanova, R. del J. P., Pifarre, K. T., & Ashraf, I. (2022). An Effective Method for Lung Cancer Diagnosis from CT Scan Using Deep Learning-Based Support Vector Network. Cancers, 14(21), 5457. https://doi.org/10.3390/CANCERS14215457
• Tárnoki, Á. D., Tárnoki, D. L., Dąbrowska, M., Knetki-Wróblewska, M., Frille, A., Stubbs, H., Blyth, K. G., & Juul, A. D. (2024). New developments in the imaging of lung cancer. Breathe, 20(1), 230176. https://doi.org/10.1183/20734735.0176-2023
• Thandra, K. C., Barsouk, A., Saginala, K., Aluru, J. S., & Barsouk, A. (2021). Epidemiology of lung cancer. Contemporary Oncology, 25(1), 45. https://doi.org/10.5114/WO.2021.103829
• Tran, T. O., Hoa Vo, T., & Khanh Le, N. Q. (2024). Omics-based deep learning approaches for lung cancer decision-making and therapeutics development. Briefings in Functional Genomics, 23(3), 181–192. https://doi.org/10.1093/BFGP/ELAD031
• Wang, L. (2022). Deep Learning Techniques to Diagnose Lung Cancer. Cancers, 14(22), 5569. https://doi.org/10.3390/CANCERS14225569
• Wani, N. A., Kumar, R., & Bedi, J. (2024). DeepXplainer: An interpretable deep learning based approach for lung cancer detection using explainable artificial intelligence. Computer Methods and Programs in Biomedicine, 243(1), 107879. https://doi.org/10.1016/J.CMPB.2023.107879
• Zhang, Y., Yang, Z., Chen, R., Zhu, Y., Liu, L., Dong, J., Zhang, Z., Sun, X., Ying, J., Lin, D., Yang, L., & Zhou, M. (2024). Histopathology images-based deep learning prediction of prognosis and therapeutic response in small cell lung cancer. Npj Digital Medicine 2024, 7(1), 1–12. https://doi.org/10.1038/s41746-024-01003-0