A novel lung cancer detection adopting Radiomic feature extraction with Locust assisted CS based CNN classifier

Abstract

Cancer is regarded as one of the life-threatening diseases since it causes significant number of fatalities in every year. Among different cancer types, the lung cancer is considered as the most destructive type with largest mortality rate. Therefore, an effective and accurate technique for detecting the lung cancer is crucial for providing the adequate treatment on time. This study presents a novel deep learning-based lung cancer detection method. The technique of image processing comprises of four major phases. Initially, the pre-processing of input images is carried out with the implementation of Adaptive Wiener filter for successfully eliminating the noises in the image without making any edge loss. Then, the process of segmentation is executed using Cascaded K-means Fuzzy C-means (KM-FCM) algorithm. The stages of feature extraction and selection are carried out using Radiomics approach, which aids in the extraction and selection of meaningful features that facilitates cancer detection. The final stage of image processing is classification, which is accomplished by a novel Locust assisted Crow Search (CS) based Convolutional Neural Network (CNN) classifier. The proposed digital image processing technique displays an impressive performance in detecting lung cancer with an accuracy of 96.33%.