Novel computer‐aided diagnosis of lung cancer using bag of visual words to achieve high accuracy rates

Abstract

Lung Cancer is considered as one of the deadliest diseases among other lung disorders. Several researchers studied and investigated about classifying the lung cancer images and their approaches do not provide better accuracy. The objective of this paper is mainly focused on detecting the cancerous region and classifying the lung cancer CT images. The proposed work plays a significant role in detecting the types of cancerous region in the lung CT images and helps radiologists to enhance the diagnosis in medical care of patients. It's also a less time consuming process in classifying the lung CT images. Bag of Visual Words (BoVW) is a novel classification technique which is applied on lung CT images to classify the cluster of begnin or malignant lung cancer in different steps. With the help of MATLAB 2016, the simulation results are obtained. The achieved accuracy of BoVW classifier is 96% which is higher than other classifiers applied on lung cancer CT images. This classifier highly reduces the computational complexity. This CAD system is very beneficial to radiologists and oncologists in cancer research centres or any cancer institutions. By applying other segmentation or novel classification techniques may improve the automatic diagnosis of lung cancer images.