Gharaibeh Nasr Y, De Fazio Roberto, Al-Naami Bassam, Al-Hinnawi Abdel-Razzak, Visconti Paolo
Department of Electrical Engineering, Al-Balqa Applied University, Salt 21163, Jordan.
Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy.
J Imaging. 2024 Jul 15;10(7):168. doi: 10.3390/jimaging10070168.
Accurate prognosis and diagnosis are crucial for selecting and planning lung cancer treatments. As a result of the rapid development of medical imaging technology, the use of computed tomography (CT) scans in pathology is becoming standard practice. An intricate interplay of requirements and obstacles characterizes computer-assisted diagnosis, which relies on the precise and effective analysis of pathology images. In recent years, pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection have witnessed the considerable potential of artificial intelligence, especially deep learning techniques. In this context, an artificial intelligence (AI)-based methodology for lung cancer diagnosis is proposed in this research work. As a first processing step, filtering using the Butterworth smooth filter algorithm was applied to the input images from the LUNA 16 lung cancer dataset to remove noise without significantly degrading the image quality. Next, we performed the bi-level feature selection step using the Chaotic Crow Search Algorithm and Random Forest (CCSA-RF) approach to select features such as diameter, margin, spiculation, lobulation, subtlety, and malignancy. Next, the Feature Extraction step was performed using the Multi-space Image Reconstruction (MIR) method with Grey Level Co-occurrence Matrix (GLCM). Next, the Lung Tumor Severity Classification (LTSC) was implemented by using the Sparse Convolutional Neural Network (SCNN) approach with a Probabilistic Neural Network (PNN). The developed method can detect benign, normal, and malignant lung cancer images using the PNN algorithm, which reduces complexity and efficiently provides classification results. Performance parameters, namely accuracy, precision, F-score, sensitivity, and specificity, were determined to evaluate the effectiveness of the implemented hybrid method and compare it with other solutions already present in the literature.
准确的预后和诊断对于肺癌治疗的选择和规划至关重要。由于医学成像技术的快速发展，计算机断层扫描（CT）扫描在病理学中的应用正成为标准做法。计算机辅助诊断的特点是需求和障碍之间复杂的相互作用，它依赖于对病理图像的精确有效分析。近年来，诸如肿瘤区域识别、预后预测、肿瘤微环境表征和转移检测等病理图像分析任务见证了人工智能，尤其是深度学习技术的巨大潜力。在此背景下，本研究工作提出了一种基于人工智能（AI）的肺癌诊断方法。作为第一步处理，使用巴特沃斯平滑滤波算法对来自LUNA 16肺癌数据集的输入图像进行滤波，以去除噪声而不显著降低图像质量。接下来，我们使用混沌乌鸦搜索算法和随机森林（CCSA-RF）方法执行双层特征选择步骤，以选择诸如直径、边缘、毛刺、分叶、细微度和恶性程度等特征。接下来，使用具有灰度共生矩阵（GLCM）的多空间图像重建（MIR）方法执行特征提取步骤。接下来，使用具有概率神经网络（PNN）的稀疏卷积神经网络（SCNN）方法实现肺癌严重程度分类（LTSC）。所开发的方法可以使用PNN算法检测良性、正常和恶性肺癌图像，这降低了复杂性并有效地提供分类结果。确定了性能参数，即准确度、精度、F分数、灵敏度和特异性，以评估所实施的混合方法的有效性，并将其与文献中已有的其他解决方案进行比较。
