Lung Cancer Classification - Sonar, DN; Kulkarni, UV (International Journal of Computer Sciences and Engineering, 2025)
Detection and diagnosis of lung cancer from chest radiographs is one of the most important and difficult task for the radiologists. In this paper, combination of statistical texture and moment invariant features are used to classify the lung cancer images. These features are extracted from JSRT raw chest X-ray images. The proposed approach is built on two-level architecture. In the first level architecture images are sharpened and segmented to extract the region of interest i.e. lung from the ribs using image processing techniques. In second level architecture, statistical texture and moment invariant based features are extracted depending on the shape characteristics of the region. These features are used as input pattern to the Fuzzy Hypersphere Neural Network (FHSNN) classifier. The experimental result shows that proposed approach is superior in comparison with only statistical texture features in terms of recognition rate, training and testing time.