Detection of Lung Cancer Using SVM Algorithm

Lung cancer detection approaches have been developed using image processing technologies. The developed system has three options for taking medical images: CT, MRI, and ultrasound. It can capture any type of medical image. The model that is being shown here was created using his PSO, genetic optimization, and SVM methods, which are employed for feature selection and classification. In order to produce feature extraction findings following segmentation and feature selection, this paper extends image processing with lung cancer diagnosis. The MRI, CT, and ultrasound are the three medical imaging input methods that the system accepts. A complex edge detection filter is applied after pre-processing the image. This study suggests a technique for accurately identifying cancer cells in CT, MRI, and ultrasound imaging. Medical picture segmentation and denoising both use the Super pixel Segmentation and Gabor Filter, respectively. MATLAB simulation results for a cancer detection system may be obtained, and three medical images can be compared.