Lung cancer is a severe menace to human health, due to which millions of people die because of late diagnoses of cancer; thus, it is vital to detect the disease as early as possible. The Computerized chest analysis Tomography of scan is assumed to be one of the efficient solutions for detecting and classifying lung nodules. The necessity of high accuracy of analyzing C.T. scan images of the lung is considered as one of the crucial challenges in detecting and classifying lung cancer. A new long-short-term-memory (LSTM) based deep fusion structure, is introduced, where, the texture features computed from lung nodules through new volumetric grey-level-co-occurrence-matrices (GLCM) computations are applied to classify the nodules into: benign, malignant and ambiguous. An improved Otsu segmentation method combined with the water strider optimization algorithm (WSA) is proposed to detect the lung nodules. Otsu-WSA thresholding can overcome the restrictions present in previous thresholding methods. Extended experiments are run to assess this fusion structure by considering 2D-GLCM computations based 2D-slices fusion, and an approximation of this 3D-GLCM with volumetric 2.5D-GLCM computations-based LSTM fusion structure. The proposed methods are trained and assessed through the LIDC-IDRI dataset, where 94.4%, 91.6%, and 95.8% Accuracy, sensitivity, and specificity are obtained, respectively for 2D-GLCM fusion and 97.33%, 96%, and 98%, accuracy, sensitivity, and specificity, respectively, for 2.5D-GLCM fusion. The yield of the same are 98.7%, 98%, and 99%, for the 3D-GLCM fusion. The obtained results and analysis indicate that the WSA-Otsu method requires less execution time and yields a more accurate thresholding process. It is found that 3D-GLCM based LSTM outperforms its counterparts.
翻译:肺癌是人体健康的一个严重威胁,由于对癌症的诊断迟缓,数百万人因此而死亡;因此,必须尽早发现这一疾病。扫描的计算机化胸腔分析被假定为检测和分类肺结核的有效解决办法之一。分析 C.T. 扫描肺部图像的高度准确性是发现和分类肺癌的关键挑战之一。 引入了一种新的短期TM(LSTM), 其基础为94. 深度聚合结构;因此, 尽早检测该疾病至关重要。 计算机化的胸部分析是检测和分类肺结核的有效方法之一。 肺部扫描图像的高度精确精确性是:检测和分类。 用于检测肺癌和分类的新短期TM(LSTM),基于 94.M.D. D.