Detection of lung tumor using dual tree complex wavelet transform and co‐active adaptive neuro fuzzy inference system classification approach

Abstract

AbstractThe automatic detection and location of the tumor regions in lung images is more important to provide timely medical treatments to patients in order to save their lives. In this article, machine learning‐based lung tumor detection, classification and segmentation algorithm is proposed. The tumor classification phase first smooth the source lung computed tomography image using adaptive median filter and then discrete time complex wavelet transform (DT‐CWT) is applied on this smoothed lung image to decompose the entire image into a number of sub‐bands. Along with the decomposed sub‐bands, DWT, pattern, and co‐occurrence features are computed and classified using co‐active adaptive neuro fuzzy inference system (CANFIS). The tumor segmentation phase uses morphological functions on this classified abnormal lung image to locate the tumor regions. The multi‐evaluation parameters are used to evaluate the proposed method. This method is compared with the other state‐of‐the‐art methods on the same lung image from open‐access dataset.