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.

肺癌是目前与癌症相关的死亡的最常见原因之一。在其发展的早期阶段检测并提供准确的潜在癌性肺结节诊断将提高治疗效果，从而降低肺癌死亡率。早期发现的关键障碍是直到肺癌已经扩散才出现明显的症状。使用非侵入性成像（例如计算机断层扫描（CT））的诊断和筛查是一种潜在的解决方案。但是，要实现这种方法的潜力，需要对这些高分辨率图像进行精确的自动化分析。图像分割是该过程的重要阶段。基于模糊的图像分割方案使用每行的最大值和每列的最小值。，。在下一阶段的分析中，神经模糊分类器将那些分割的肺结节分为恶性和良性结节。敏感性，特异性和准确性用作性能评估参数。所提出的方法导致灵敏度，特异性，精确度和准确性分别为100％，81％，86％和90％，假阳性率降低。总而言之，我们改进的算法可以显着提高CT成像对早期患者的诊断准确性。因此，我们的方法可以为肺癌患者带来更好的临床结果。