Morphological active contour based SVM model for lung cancer image segmentation

Lung cancer is currently the leading cause of cancer-related death. To lower the mortality from lung cancer, early detection is crucial. A precise and effective method of diagnosis by medical professionals is necessary for early detection of lung-related cancers in order to maximize the success rate of treatment. It is particularly difficult to catch it in the early stages of dissemination because there are no symptoms in the early stages. Using specific image processing ideas on computed tomography (CT), we can identify the tumor state in the early phases of spread and diagnose this at an early level. The early detection is crucial for curing and restraining the spread of uncontrolled malignant cells. The best outcomes for accurately identifying malignant tumors in their early stages come from combining the effects of morphological filtering and a contour-based picture segmentation technique. The segmented lung CT scan pictures that were malignant were separated from the rest of the testing dataset by a support vector machine (SVM) classifier in the subsequent analysis phase. Various image quality and performance metrics, such as accuracy, sensitivity, precision and specificity are used to validate the proposed technique.