Content Based Image Retrieval System for Lung Cancer Detection Using Neural Network and Circular Hough Transform

Abstract

Computer Aided Detection (CAD) systems that automatic detection and localize lung nodules in CT scans. A major problem in this system is a large number of false positives because of no provision for comparison of the predicted output. This paper recommends a new system with a combination of CBIR and neural network to full fill the gap in the area of early detection of lung cancer. From the preprocessed CT scan image, the system identifies whether it contains nodules using Circular Hough Transform and classifies into benign or malignant nodule using Probabilistic Neural Network. Then, it searched for the most identical pictures and retrieved it from the database. From the retrieved image, it is easy to identify the present cancer stage of the patient. Experiments have done based on both LIDC database and the locally collected database. The performance evaluation of the system is done by using both. The experimental results show that the present study easily differentiates benign and malignant nodules with an efficiency of 97 % accuracy on LIDC dataset, 95 % accuracy on Local dataset and similar images are retrieved with its present stage from the available database with a higher precision and recall rate.