Optimized H–Inspired Algorithm and SVM Classification For Lung Tumor Detection

One of the most deadly and pervasive diseases is cancer. Among all the main cancers, lung cancer is the most prevalent and has the best rate of survival. CT scans are used to identify the matching tumor, give a thorough image of the lung tumor, and monitor the tumor’s progression. Despite the fact that CT scans are preferred to other imaging methods, visual interpretation of these scans’ pictures can be challenging and delay the detection of tumors. In order to detect lung tumors early, image processing techniques are commonly used in the medical industry. An improved and hybrid technique to lung tumor detection in CT images is presented in this research. In this study, we suggested one approach that improved the one already in use. Gabor & Global Thresholding Algorithm (G
²
TA) and Particle Swarm optimization make up the new algorithm (PSO). The G
²
TA-PSO Algorithm is the name of the suggested algorithm.