Predictive Modeling of Lung Cancer using Support Vector Machine

The segmentation of lung cancer regions in medical images is a crucial part of computer-aided diagnosis systems. Describe a technique in this paper that achieves this goal by employing the Support Vector Machine (SVM) algorithm. Training an SVM on a split dataset involves using feature vectors generated from the retrieved features. The testing set's regions are subsequently classified using the trained SVM, and the segmentation outcomes are then improved by using post-processing techniques. The performance of the method is evaluated using metrics including dice coefficient, precision, recall, and F1 score. The proposed SVM-based algorithm serves as a fundamental tool for lung cancer segmentation, contributing to the ongoing advancements in computer-aided diagnosis and medical image analysis.