Enhanced Prediction of Lung Cancer Stages using SVM and Medical Imaging

This study presents a comprehensive advance that be SVM, KNN, and DS method do get for lung cancer segmentation in aesculapian images. Lineament vectors created from excerpt feature of speech and aim on a metameric dataset were the world-class exercise of the SVM technique. Data Point structure and KNN algorithms are and then utilize to classify the examination set 's region, and post - processing techniques are applied to enhance segmentation. Among the evaluation standard employ to gauge the efficacy of the fuse approach path are accuracy, recall, F1 sexual conquest, and dice coefficient. Combining the SVM, DS, and KNN algorithm creates a flexible framework for lung cancer segmentation that importantly ameliorate electronic computer - aided diagnosis and medical picture analysis.