Lung Cancer Detection Using Wavelet Scattering Transform and Artificial Intelligence Technique

Abstract

The diagnosis of lung cancer is practically done by looking at a sample of lung cells in the lab. However, signs of lung cancer can be found by screening using X-Ray, CT, or Histopathological images. Each of these imaging modalities has its advantages and disadvantages. Chest X-ray is the first-line investigation for suspected lung cancer in primary care. However, the highest-quality studies suggest that the sensitivity of chest X-rays for symptomatic lung cancer is only 77–80%. On the other hand, a chest CT scan uses x-rays to make detailed cross-sectional images of the chest. Instead of taking 1 or 2 pictures, like a regular x-ray, a CT scanner takes many pictures and a computer then combines them to show a slice of the part of the chest under investigation. A CT scan is more likely to show lung tumors than traditional chest x-rays. In addition, the size, shape, and position of any lung tumors can be shown by a chest CT scan. More lung cancers were detected in the CT screening group compared with the control group with a 95% confidence interval. Moreover, Histopathological image analysis is widely used for cancer grading. Compared to mammography, CT and others, histopathology slides provide more comprehensive information for the diagnosis, and the diseases are analyzed by detecting tissue and cells in lesions. However, an invasive biopsy is necessary, which is often tried to be avoided. Therefore, chest CT is an optimal candidate for our study in sense of accuracy and availability. In this proposal, we deal with multi-class cancer detection from CT Lung images that is by detecting the cancer type rather than two classes (cancerous and normal images). The proposed method is based on a better representation of the image features by using Wavelet scattering Transform (WST). The classification is performed using three machine learning (ML) algorithms including support vector machine (SVM), kernel nearest neighbor (KNN), and random forest (RF). The WST coefficients are stable under signal deformations and globally invariant to signal translation and rotation. Based on the simulation results, the proposed method achieved an accuracy of 93.24%, 95.28%, and 99.90% for the case of WST + SVM, WST + KNN, and WST + RF networks, respectively.