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 studies using ML algorithms to identify lung cancer have been conducted. Most often, a multi-stage classification is employed to predict lung cancer. The segmentation and data improvement classification scheme have 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.
At our project centers in Bangalore, we provide the following components for IEEE final year projects on lung cancer detection:
1. Complete documentation support for IEEE projects and final year projects.
2. Complete working hardware/software implemented in the student's environment.
3. Project classes will be held accordingly to guide and support the students in their final year projects.
Our project centers in Bangalore specialize in providing comprehensive support and resources for students working on lung cancer detection projects as part of their final year.