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@article{177631,
        author = {ADITI SINGH and JUHI SINGH and RAGHURAJ SINGH and MILLAN SAXENA},
        title = {Lung Cancer Detection Using Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {1368-1372},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177631},
        abstract = {Lung cancer is one of the major causes of death worldwide and its early and accurate diagnosis is crucial to improving survival rates. This paper focuses on the use of deep learning, such as Convolutional Neural Networks (CNN) to automatically classify lung cancer images as benign, malignant, or normal. We have trained a custom CNN model using the IQ-OTHNCCD dataset after applying pre-processing techniques to boost accuracy and reliability. The model performed exceptionally well, achieving 99.09% accuracy on the test set, with very low loss values. The results show that deep learning can effectively support the diagnostic process by making it faster and consistent. This AI-powered approach could help doctors catch lung cancer accurately at an earlier stage, potentially improving patient care and outcomes.},
        keywords = {AI in Healthcare, Benign vs. Malignant, Cancer Detection, CNN, Deep Learning.},
        month = {May},
        }