The automatic detection and location of the tumor regions in lung images is more important to provide timely medical treatments to patients in order to save their lives. In this article, machine learning-based lung tumor detection, classification and segmentation algorithm is proposed. The tumor classification phase first smooth the source lung computed tomography image using adaptive median filter and then discrete time complex wavelet transform (DT-CWT) is applied on this smoothed lung image to decompose the entire image into a number of sub-bands. Along with the decomposed sub-bands, DWT, pattern, and co-occurrence features are computed and classified using co-active adaptive neuro fuzzy inference system (CANFIS). The tumor segmentation phase uses morphological functions on this classified abnormal lung image to locate the tumor regions. The multi-evaluation parameters are used to evaluate the proposed method. This method is compared with the other state-of-the-art methods on the same lung image from open-access dataset.

中文翻译：

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使用双树复小波变换和协同自适应神经模糊推理系统分类方法检测肺肿瘤

肺部图像中肿瘤区域的自动检测和定位对于为患者提供及时的医疗以挽救他们的生命更为重要。本文提出了基于机器学习的肺肿瘤检测、分类和分割算法。肿瘤分类阶段首先使用自适应中值滤波器平滑源肺计算机断层扫描图像，然后在这个平滑的肺图像上应用离散时间复小波变换 (DT-CWT)，将整个图像分解为多个子带。与分解的子带一起，使用协同自适应神经模糊推理系统 (CANFIS) 计算和分类 DWT、模式和共现特征。肿瘤分割阶段在这个分类的异常肺图像上使用形态学函数来定位肿瘤区域。多评估参数用于评估所提出的方法。在来自开放访问数据集的同一肺图像上，将此方法与其他最先进的方法进行比较。