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@article{188940,
        author = {Prathap and Dr. Sanjeev Kulkarni},
        title = {Lung Cancer Prediction Using Deep Learning Modalities},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {4094-4127},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=188940},
        abstract = {At the point when cells in the body develop out of control, this is alluded to as cancerous development. Lung cancer is the term used to depict cancer that starts in the lungs. At first in the field, classifier-based approaches are joined with various division calculations to utilize picture acknowledgment to recognize lung cancer nodules. This study found that CT scan images are more reasonable for delivering improved results than other imaging modalities. The use of the images is a piece of chiefly inspecting the CT scanned images that are viewed as informational collections for patients affected by lung cancer. The suggestion of our paper exclusively centers around the execution of concentrating on the calculation’s accuracy in diagnosing lung cancer. Thus, the primary plan of our examination is to utilize examined calculations to conclude which strategy is the most efficient method for detecting lung cancer initially. After training the model we found that Over all accuracy of Resnet-18 is 99.54%, the Overall accuracy of Vgg-19 is 96.35%, The overall accuracy of MobileNet V2 is 98.17%, Dense Net161 is 99.09% and Inception V3 is 98.17%. So we can see that ResNet18 perform better than other train model},
        keywords = {Hog Feature Extraction, Lung Cancer, Deep learning, ResNet18, DeneNet161, MobileNetV2, ShuffleNet, InceptionV3, VGG19.},
        month = {December},
        }