Lung cancer detection using enhanced segmentation accuracy

Lung cancer is currently one of the most common causes of cancer-related death. Detecting and providing an accurate diagnosis of potentially cancerous lung nodules at an early stage of their development would increase treatment efficacy and so reduce lung cancer mortality. A key barrier to early detection is the absence of noticeable symptoms until the lung cancer has already spread. Diagnosis and screening using non-invasive imaging such as computed tomography (CT) is a potential solution. However, to realize the potential of this approach an accurate automated analysis of these high-resolution images needed. Image segmentation is an important stage of that process. Fuzzy-based image segmentation schemes use the maximum of each row and minimum of each column. Our study developed an algorithm that employs median values measured along each row and column, in addition to the maxima and minima values, and found that this approach increased segmenting accuracy of these images,. In the next phase of analysis, a neuro-fuzzy classifier classified those segmented lung nodules into malignant and benign nodules. Sensitivity, specificity and accuracy were used as performance assessment parameters. The proposed methodology resulted in sensitivity, specificity, precision and accuracy of 100%, 81%, 86% and 90%, respectively, with a reduced false positive rate. In sum, our improved algorithm can give significantly improved accuracy of diagnosis in early-stage patients from CT imaging. Thus, our methodology could contribute to better clinical outcomes for lung cancer patients.