An efficient lung image classification and detection using spiral-optimized Gabor filter with convolutional neural network

Abstract

Lung cancer has a high death rate of around seven million cases every year worldwide. A computed tomography (CT) scan provides certain essential data concerning lung diseases and their diagnosis. The main objective of this work is to classify various lung diseases such as Normal, Bronchiectasis, and Pleural Effusion. The proposed approach consists of three stages, namely pre-processing, feature extraction, and classification. At first, CT lung images are collected from the dataset and pre-processed. After pre-processing, the important texture features are extracted from each image. For feature extraction, spiral-optimized Gabor filter (SOGF) is utilized. The proposed SOGF is a combination of spiral optimization algorithm (SOA) and Gabor filter (GF). Then, the extracted features are given to the convolutional neural network (CNN) to classify different types of lung diseases. For comparison, we use different classifiers, namely artificial neural network (ANN), Random Tree, and the Naïve Bayes. The experimental results show that our proposed approach attained the maximum accuracy of 93.67% which is high compared to other methods.