Artificial Intelligence Department, Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran.
Faculty of Computer Science and Mathematics, University of Thi-Qar, Nasiriyah, Thi-Qar, Iraq.
PLoS One. 2022 Sep 29;17(9):e0274516. doi: 10.1371/journal.pone.0274516. eCollection 2022.
Lung cancer is a serious threat to human health, with millions dying because of its late diagnosis. The computerized tomography (CT) scan of the chest is an efficient method for early detection and classification of lung nodules. The requirement for high accuracy in analyzing CT scan images is a significant challenge in detecting and classifying lung cancer. In this paper, a new deep fusion structure based on the long short-term memory (LSTM) has been introduced, which is applied to the texture features computed from lung nodules through new volumetric grey-level-co-occurrence-matrices (GLCMs), classifying the nodules into benign, malignant, and ambiguous. Also, an improved Otsu segmentation method combined with the water strider optimization algorithm (WSA) is proposed to detect the lung nodules. WSA-Otsu thresholding can overcome the fixed thresholds and time requirement restrictions in previous thresholding methods. Extended experiments are used to assess this fusion structure by considering 2D-GLCM based on 2D-slices and approximating the proposed 3D-GLCM computations based on volumetric 2.5D-GLCMs. The proposed methods are trained and assessed through the LIDC-IDRI dataset. The accuracy, sensitivity, and specificity obtained for 2D-GLCM fusion are 94.4%, 91.6%, and 95.8%, respectively. For 2.5D-GLCM fusion, the accuracy, sensitivity, and specificity are 97.33%, 96%, and 98%, respectively. For 3D-GLCM, the accuracy, sensitivity, and specificity of the proposed fusion structure reached 98.7%, 98%, and 99%, respectively, outperforming most state-of-the-art counterparts. The results and analysis also indicate that the WSA-Otsu method requires a shorter execution time and yields a more accurate thresholding process.
肺癌是对人类健康的严重威胁，数以百万计的人因诊断过晚而死亡。胸部计算机断层扫描（CT）是早期发现和分类肺结节的有效方法。分析 CT 扫描图像需要高精度，这是检测和分类肺癌的重大挑战。在本文中，提出了一种基于长短期记忆（LSTM）的新的深度融合结构，该结构应用于通过新的体积灰度共生矩阵（GLCM）计算的肺结节的纹理特征，将结节分为良性、恶性和不确定。此外，还提出了一种改进的 Otsu 分割方法，结合水黾优化算法（WSA）来检测肺结节。WSA-Otsu 阈值法可以克服以前阈值法中的固定阈值和时间要求限制。通过考虑基于 2D-切片的 2D-GLCM 和基于体素 2.5D-GLCM 近似的 3D-GLCM 计算，扩展实验来评估这种融合结构。所提出的方法通过 LIDC-IDRI 数据集进行训练和评估。基于 2D-GLCM 融合的准确率、灵敏度和特异性分别为 94.4%、91.6%和 95.8%。对于 2.5D-GLCM 融合，准确率、灵敏度和特异性分别为 97.33%、96%和 98%。对于 3D-GLCM，所提出的融合结构的准确率、灵敏度和特异性分别达到 98.7%、98%和 99%，优于大多数最先进的方法。结果和分析还表明，WSA-Otsu 方法需要更短的执行时间，并产生更准确的阈值处理过程。
