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@article{170727,
        author = {Dr. Amjad Khan},
        title = {Performance of VGG-19 Convolutional Neural Network Model Based Lung Cancer Classification on Computed Tomography},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {1614-1619},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=170727},
        abstract = {The heterogeneity of the lung nodules and the complexity of the surrounding environment have made the robust nodule detection challenging task in identification of lung cancer. The survival rate of lung cancer depends upon early identification of lung nodules which is an efficient way to minimize the death rate of patients. The proposed method for Lung nodule classification from CT images developed using VGG-19 convolutional neural networks. This method eliminates the need of manual feature extraction as depicted in the feedback of previous work. The network is fed with raw lung CT images from publicly available LIDC-IDRI dataset. Here, the lung images are classified into two classes such as benign and malignant. This classification is achieved with the help of VGG-19 convolutional neural network. This method successfully classified the lung CT images into two classes and achieved 86% accuracy with comparatively less false positive rates.},
        keywords = {Lung Cancer, VGG-19, Computed Tomography, Classification},
        month = {December},
        }