Optimized Hyper parameter Approach for Lung Cancer Prediction and classification of types Using GLCM
Keywords:
Area Under Curve, Benign, Hyper parameter, Lung Cancer, Lung Imaging Database Consortium Malignant, Optimization.Abstract
Cancer is one of the most lethal illnesses, causing an uncountable number of deaths worldwide. Diagnosing lung cancer at an earlier stage has attracted significant interest from medical professionals. This research introduces a novel method for detecting lung cancer by processing images obtained from CT scans. Utilizing cases from the Lung Imaging Database Consortium (LIDC) database, this study evaluates the feasibility of applying algorithms to detect lung cancer. The primary aim of this study is to determine whether the tumors found in the lung are malignant or benign. This is accomplished using the GLCM feature extractor and the SVM classifier. The research proposes a hyper parameter optimization approach for identifying lung cancer and classification of its stages. The results of the hyper parameter optimization show an overall prediction accuracy of 95.86%, with more than 90% accuracy in classifying tumors as malignant, benign, or normal. The area under the curve (AUC) values are more than 90% for the malignant, benign, and normal classes, respectively. In the classification of lung cancer and its types including 91.1% for Adenocarcinoma, 91.5% for Large cell carcinoma, 92.9% for Squamous cell carcinoma, and 99.2% for Normal lung tissue.
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