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Lung cancer detection

Abstract:
One of the most prevalent diseases that is affected in its early stages to increase the likelihood that patients will survive is lung disease. The hardest aspect for the radiologist is making the cancer diagnosis. A smart computer-aided system is tremendously beneficial to radiologists. several research using ML algorithms to identify lung cancer. Most often, a multi-stage classification is employed to predict lung cancer. The segmentation and data improvement classification scheme has been completed. Threshold and marker-controlled watershed and binary classifier are used in the segmentation approach. The accuracy of lung cancer detection is higher. The dataset is trained using a variety of techniques, including Support Vector Machine (SVM), K-Nearest Neighbor, Decision Tree, Logistic Regression, Naive Bayes, and Random Forest, and it is demonstrated that these approaches are more accurate. The Random forest method has generated improved performance with 88.5% accuracy.
Components that we will be providing including project.
1.complete documentation support
2.complete working hardware/software implemented in students environment
3.project classes will held accordingly.

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