Multi-Model Detection of Lung Cancer Using Unsupervised Diffusion Classification Algorithm

N Jayanthi,D Manohari,C Bharatiraja,Mohamed Ibrahim Waly,Mohamed Abdelkader Aboamer,Mohamed Yacin Sikkandar

doi:10.32604/iasc.2022.018974

Abstract

Lung cancer is a curable disease if detected early, and its mortality rate decreases with forwarding treatment measures. At first, an easy and accurate way to detect is by using image processing techniques on the cancer-affected images captured from the patients. This paper proposes a novel lung cancer detection method. Firstly, an adaptive median filter algorithm (AMF) is applied to preprocess those images for improving the quality of the affected area. Then, a supervised image edge detection algorithm (SIED) is presented to segment those images. Then, feature extraction is employed to extract the mean, standard deviation, energy, contrast, etc., of the cancer-affected area. Finally, an unsupervised diffusion classification (UDC) algorithm is explored to narrow down the affected areas. The proposed lung cancer detection method is implemented on two datasets obtained from standard hospital real-time values. The experiment results achieved superior performance in the detection of lung cancer, which demonstrates that our new model can contribute to the early detection of lung cancer.

Highlights

Lung cancer is one of the most dangerous diseases afflicting people
The detection of the affected cancer region in the lungs with the help of the image processing technique is based on the proposed unsupervised diffusion classification (UDC) technique
The proposed unsupervised diffusion classification (UDC) performs superior by accomplishing exactness, affectability, and explicitness when contrasted with another ordinary classifier

Summary

Introduction

Lung cancer is one of the most dangerous diseases afflicting people. It can typically be diagnosed using image processing techniques to quickly identify affected cancer areas, thereby decreasing decrease its development in time. Several factors may influence a rapid diagnosis of lung cancer, including tumor growth, late mortality due to uncertain efficacy, lack of specific screening, and rapid disease progression symptoms. The disease diagnosis depends, as well, on its performance dates. In the past few decades, the accuracy of these values has been declining, and slowing down the pace of the fight against. Until recently, most lung cancers are being treated more or less as the same disease, without any distinction to their variability

Methods

Results

Conclusion