A quarter of all cancer deaths are due to lung cancer. Studies show that early diagnosis and treatment of this disease are the most effective way to increase patient life expectancy. In this paper, automatic and optimized computer-aided detection is proposed for lung cancer. The method first applies a preprocessing step for normalizing and denoising the input images. Afterward, Kapur entropy maximization is performed along with mathematical morphology to lung area segmentation. Afterward, 19 GLCM features are extracted from the segmented images for the final evaluations. The higher priority images are then selected for decreasing the system complexity. The feature selection is based on a new optimization design, called Improved Thermal Exchange Optimization (ITEO), which is designed to improve the accuracy and convergence abilities. The images are finally classified into healthy or cancerous cases based on an optimized artificial neural network by ITEO. Simulation is compared with some well-known approaches and the results showed the superiority of the suggested method. The results showed that the proposed method with 92.27% accuracy provides the highest value among the compared methods.

中文翻译：

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基于改进 TEO 算法优化的 ANN 的肺癌诊断

四分之一的癌症死亡是由肺癌引起的。研究表明，早期诊断和治疗这种疾病是提高患者预期寿命的最有效方法。在本文中，针对肺癌提出了自动和优化的计算机辅助检测。该方法首先应用预处理步骤对输入图像进行归一化和去噪。然后，将 Kapur 熵最大化与数学形态学一起进行肺区域分割。之后，从分割图像中提取 19 个 GLCM 特征用于最终评估。然后选择更高优先级的图像以降低系统复杂性。特征选择基于新的优化设计，称为改进的热交换优化 (ITEO)，旨在提高准确性和收敛能力。图像最终根据ITEO优化的人工神经网络分为健康或癌症病例。仿真与一些众所周知的方法进行了比较，结果表明了所建议方法的优越性。结果表明，所提出的方法以 92.27% 的准确率提供了比较方法中的最高值。