近年来的研究表明，非传染性疾病如癌症的传播有所增加。肺癌的诊断和检测在近年来已成为一个主要障碍。早期的肺癌诊断和检测将可可靠地促进全球范围内许多生命的安全和生存。利用医学图像进行精确定类化肺癌将有助于医生选择合适的治疗方法，降低癌症死亡率。在使用卷积神经网络(CNN)进行肺癌检测的领域有许多工作已进行。然而，由于CT扫描中的多方面设计，肺癌的预测仍然具有困难。此外，CNN模型存在一些问题会影响它们的性能，包括选择最佳的架构，选择适当的模型参数，以及选择最佳的权重和偏置值。为解决CT图像中选择用于肺癌分类的最佳权重和偏置组合的问题，本研究提出了一个混合元启发式和CNN算法。我们首先设计了一个CNN架构，然后计算了模型的解向量。得到的解向量被传递给埃博拉优化搜索算法(EOSA)，以选择最佳的权重和偏置组合来训练CNN模型以处理分类问题。经过彻底训练EOSA-CNN混合模型后，我们得到了最佳配置，其表现良好。通过对公开可访问的伊拉克肿瘤教学医院/全国癌症疾病中心(IQ-OTH/NCCD)肺癌数据集进行实验，结果显示EOSA元启发式算法的分类准确率为0.9321。同样地，在性能比较方面，EOSA-CNN与其他方法GA-CNN、LCBO-CNN、MVO-CNN、SBO-CNN、WOA-CNN和经典CNN的比较也进行了计算和呈现。结果表明，EOSA-CNN在正常、良性和恶性病例中分别获得了0.7941、0.97951和0.9328的特异性，以及0.9038、0.13333和0.9071的敏感性。这证实了该混合算法在肺癌分类方面提供了较好的解决方案。版权所有：©2023 Mohamed等。本文是根据Creative Commons Attribution License(知识共享许可证)开放获取的文章，允许在任何媒介中自由使用、分发和复制，前提是原作者和来源得到了适当的认可。

Recently, research has shown an increased spread of non-communicable diseases such as cancer. Lung cancer diagnosis and detection has become one of the biggest obstacles in recent years. Early lung cancer diagnosis and detection would reliably promote safety and the survival of many lives globally. The precise classification of lung cancer using medical images will help physicians select suitable therapy to reduce cancer mortality. Much work has been carried out in lung cancer detection using CNN. However, lung cancer prediction still becomes difficult due to the multifaceted designs in the CT scan. Moreover, CNN models have challenges that affect their performance, including choosing the optimal architecture, selecting suitable model parameters, and picking the best values for weights and biases. To address the problem of selecting optimal weight and bias combination required for classification of lung cancer in CT images, this study proposes a hybrid metaheuristic and CNN algorithm. We first designed a CNN architecture and then computed the solution vector of the model. The resulting solution vector was passed to the Ebola optimization search algorithm (EOSA) to select the best combination of weights and bias to train the CNN model to handle the classification problem. After thoroughly training the EOSA-CNN hybrid model, we obtained the optimal configuration, which yielded good performance. Experimentation with the publicly accessible Iraq-Oncology Teaching Hospital / National Center for Cancer Diseases (IQ-OTH/NCCD) lung cancer dataset showed that the EOSA metaheuristic algorithm yielded a classification accuracy of 0.9321. Similarly, the performance comparisons of EOSA-CNN with other methods, namely, GA-CNN, LCBO-CNN, MVO-CNN, SBO-CNN, WOA-CNN, and the classical CNN, were also computed and presented. The result showed that EOSA-CNN achieved a specificity of 0.7941, 0.97951, 0.9328, and sensitivity of 0.9038, 0.13333, and 0.9071 for normal, benign, and malignant cases, respectively. This confirms that the hybrid algorithm provides a good solution for the classification of lung cancer.Copyright: © 2023 Mohamed et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.