An Optimized VGG-19 Architecture Integrated with Support Vector Machines for Lung Cancer Detection and Classification

Abstract

Lung cancer is an identical serious and deadly disease, normally signaled by small growths in the lungs called nodules. It usually happens because cells in the lung start increasing uncontrollably. Finding these lung nodules is important for detecting lung cancer, often done using CT scans. Catching the disease early significantly improves the chances of effective treatment and survival. To recuperate lung cancer detection, this study introduces an automated method for finding nodules in CT images, called Enhanced Visual Geometry Group (EVGG-SVM). This method uses an improved version of a well-known neural network model (VGG19) combined with a Support Vector Machine (SVM) to classify nodules as either inoffensive or cancerous. The proposed model was evaluated using the well-known LUNA16 dataset and demonstrated high levels of accuracy, sensitivity, and specificity. In comparison with other current techniques, the EVGG-SVM model demonstrated remarkable performance.