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@article{200436,
        author = {Karan Singh and Ashish Gupta and Sarwajeet Singh and Rajneesh Kumar and Dr. Devesh Garg and Mr. Prakash Joshi},
        title = {Lung Cancer Detection System Using Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {1730-1734},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=200436},
        abstract = {Lung cancer continues to be one of the most common causes of cancer death, and early diagnosis is key to enhancing survival rates. Conventional diagnostic approaches, like visual inspection of CT scans, are labor-intensive and susceptible to errors. This work proposes a deep learning-based automated lung cancer detection system using Convolutional Neural Networks (CNNs) to assess CT scans. The developed model is trained on labelled data to detect and classify lung nodules as benign or malignant. Preprocessing methods, including denoising and augmentation, are employed to enhance image quality and training data, respectively, and to improve detection accuracy. The system also includes an interface that connects to a cloud server, enabling clinicians to upload CT scans and get real-time results. This enhances user-friendliness and facilitates the system's use in areas lacking medical expertise. Our experimental findings reveal that the model attained an accuracy of 94.36% on test data, suggesting that it is capable of accurately detecting lung nodules, with high precision and recall. The developed system can help medical professionals in early diagnosis, reduce their burden, and enhance clinical decision-making.},
        keywords = {Lung Cancer, Deep Learning, Convolutional Neural Networks (CNN), Medical Imaging, CT Scan Classification, Automatic Diagnosis, Computer-Aided Detection (CAD), Early Cancer Diagnosis, Artificial Intelligence in Medicine.},
        month = {May},
        }