Forecasting Lung Cancer Using Convolution Neural Network
Keywords:
Lung Cancer, Deep Learning, CNNs, Transfer Learning, Medical Imaging, Predictive Analytics, Diagnostic AccuracyAbstract
Lung cancer is a major cause of mortality globally, underscoring the need for precise prediction methods to reduce death rates. The application of artificial intelligence (AI) to CT scan images has shown significant potential in enhancing lung cancer predictions through automated processes. Among AI techniques, deep learning, particularly Convolutional Neural Networks (CNNs), stands out in predictive analytics, frequently outperforming other machine learning algorithms. This study analysed a dataset of 1,000 chest CT scan images representing different types of lung cancer, including Adenocarcinoma, Benign, and Squamous Cell Carcinoma. Various machine learning algorithms were evaluated, with CNNs achieving the highest prediction accuracy. The existing system, which employs the VGG-16 model, achieves an accuracy of 77.62%, which is considered suboptimal. To address this, the proposed system uses the VGG-19 transfer learning model on the dataset, aiming to improve prediction accuracy and offer better insights into the severity and necessary precautions for different lung cancer types.
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