Department of Information Technology, University of Gujrat, Gujrat, Pakistan.
Department of Information Technology, Division of Science and Technology University of Education, Township Campus Lahore, Lahore, Pakistan.
Comput Intell Neurosci. 2023 Jul 26;2023:6357252. doi: 10.1155/2023/6357252. eCollection 2023.
Lung cancer is one of the deadliest cancers around the world, with high mortality rate in comparison to other cancers. A lung cancer patient's survival probability in late stages is very low. However, if it can be detected early, the patient survival rate can be improved. Diagnosing lung cancer early is a complicated task due to having the visual similarity of lungs nodules with trachea, vessels, and other surrounding tissues that leads toward misclassification of lung nodules. Therefore, correct identification and classification of nodules is required. Previous studies have used noisy features, which makes results comprising. A predictive model has been proposed to accurately detect and classify the lung nodules to address this problem. In the proposed framework, at first, the semantic segmentation was performed to identify the nodules in images in the Lungs image database consortium (LIDC) dataset. Optimal features for classification include histogram oriented gradients (HOGs), local binary patterns (LBPs), and geometric features are extracted after segmentation of nodules. The results shown that support vector machines performed better in identifying the nodules than other classifiers, achieving the highest accuracy of 97.8% with sensitivity of 100%, specificity of 93%, and false positive rate of 6.7%.
肺癌是全球最致命的癌症之一，其死亡率比其他癌症高。晚期肺癌患者的生存率非常低。然而，如果能够早期发现，患者的生存率可以得到提高。由于肺部结节与气管、血管和其他周围组织的视觉相似性，导致肺部结节的分类错误，因此早期诊断肺癌是一项复杂的任务。以前的研究使用了有噪声的特征，这使得结果不可靠。为了解决这个问题，提出了一种预测模型来准确地检测和分类肺结节。在提出的框架中，首先在肺部图像数据库联盟（LIDC）数据集的图像中进行语义分割，以识别结节。分类的最优特征包括方向梯度直方图（HOG）、局部二值模式（LBP）和几何特征，这些特征是在结节分割后提取的。结果表明，支持向量机在识别结节方面的表现优于其他分类器，其最高准确率为 97.8%，灵敏度为 100%，特异性为 93%，假阳性率为 6.7%。
