Early Diagnosis of Lung Cancer Using Computer Aided Detection via Lung Segmentation Approach

TL;DR

This paper proposes a novel computer-aided detection method for early lung cancer diagnosis using lung segmentation techniques, aiming to improve accuracy and enable earlier treatment interventions.

Contribution

It introduces a new lung segmentation approach combining Fuzzy C-Means, Adaptive Thresholding, and Active Contour Model for better early detection of lung cancer.

Findings

Segmentation accuracy improved with the proposed method

Early diagnosis potential demonstrated through experimental analysis

Enhanced detection performance over existing techniques

Abstract

Lung cancer begins in the lungs and leading to the reason of cancer demise amid population in the creation. According to the American Cancer Society, which estimates about 27% of the deaths because of cancer. In the early phase of its evolution, lung cancer does not cause any symptoms usually. Many of the patients have been diagnosed in a developed phase where symptoms become more prominent, that results in poor curative treatment and high mortality rate. Computer Aided Detection systems are used to achieve greater accuracies for the lung cancer diagnosis. In this research exertion, we proposed a novel methodology for lung Segmentation on the basis of Fuzzy C-Means Clustering, Adaptive Thresholding, and Segmentation of Active Contour Model. The experimental results are analysed and presented.