Prediction and Classification of CT images for Early Detection of Lung Cancer Using Various Segmentation Models

Abstract

One of the most serious and deadly diseases in the world is lung cancer. On the other hand, prompt diagnosis, as well as care, could save lives. Probably the most capable imaging method in the medical world, computed tomography (CT) scans are challenging for clinicians to analyze as well as detect cancer. In recent years, there has been an increase in the use of image analysis techniques for the detection of CT scan images matching cancer tissues. Using a Computer-aided detection (CAD) system employing CT scans to aid inside the early lung cancer diagnosis as well as to differentiate among benign/malignant tumors is thus interesting to address. The primary objective of this study would be to assess several computer-aided approaches, analyze the right methodology already in use, and afterward propose a new approach that integrates enhancements to the best system currently in use. This research improves the performance of the existing retrieval system by combining various image feature extraction processes and modifying the internal layer section of the classifier. The segmentation method proposed here to identify cancer is Improved Random Walker segmentation along with Random Forest (RF) classifier and K-Nearest Neighbors (KNN) classifier. Here, the research is accomplished on the Lung Image database consortium (LIDC) datasets which is a collection of CT images and is utilized as the input images to verify the effectiveness of the suggested strategy. The accuracy of the proposed method for the detection of lung cancer with the aid of the RF classifier is 99.6 % as well as the KNN classifier is 96.4% accordingly.