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@article{204208,
        author = {Dr. K.Sathya and Ms.Sowmya B},
        title = {A Systematic Review of Machine Learning and Deep Learning Techniques for Lung Cancer Prediction},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {2552-2557},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204208},
        abstract = {Lung cancer is the foremost cause of cancer-related mortality worldwide, approximately 1.8 million deaths annually. Timely and accurate detection is pivotal to improving patient outcomes. Over the Long period, the field of AI-assisted lung cancer prediction witnessed a paradigm shift from conventional machine learning approaches toward deep learning architectures—including convolutional neural networks (CNNs), vision transformers (ViTs), and, most recently, foundation models. This review systematically examines the methodological evolution, benchmark datasets, reported performance metrics, and outstanding challenges in ML- and DL-based lung cancer prediction. We cover nodule detection, malignancy classification, tumor segmentation, and survival prognosis prediction across CT, chest X-ray, and multi-modal data modalities. Key findings include: (i) 3D CNNs achieve accuracies of 94–98% on the LIDC-IDRI benchmark; (ii) transformer-based models exhibit superior performance on large datasets but demand substantial computational resources; (iii) federated learning and differential privacy are emerging solutions to multi-site data governance; and (iv) explainability (XAI) via Grad-CAM and SHAP is increasingly mandated for clinical translation. Critical gaps remain in external validation, benchmark standardisation, and regulatory approval pathways.},
        keywords = {lung cancer prediction; convolutional neural network; deep learning; CT imaging; nodule detection; vision transformer; federated learning; explainable AI; LIDC-IDRI; radiomics},
        month = {June},
        }