In this paper, we have proposed a hybrid bio-inspired algorithm which takes the merits of whale optimization algorithm (WOA) and adaptive particle swarm optimization (APSO). The proposed algorithm is referred as the hybrid WOA_APSO algorithm. We utilize a convolutional neural network (CNN) for classification purposes. Extensive experiments are performed to evaluate the performance of the proposed model. Here, pre-processing and segmentation are performed on 120 lung CT images for obtaining the segmented tumored and non-tumored region nodule. The statistical, texture, geometrical and structural features are extracted from the processed image using different techniques. The optimized feature selection plays a crucial role in determining the accuracy of the classification algorithm. The novel variant of whale optimization algorithm and adaptive particle swarm optimization, hybrid bio-inspired WOA_APSO, is proposed for selecting optimized features. The feature selection grouping is applied by embedding linear discriminant analysis which helps in determining the reduced dimensions of subsets. Twofold performance comparisons are done. First, we compare the performance against the different classification techniques such as support vector machine, artificial neural network (ANN) and CNN. Second, the computational cost of the hybrid WOA_APSO is compared with the standard WOA and APSO algorithms. The experimental result reveals that the proposed algorithm is capable of automatic lung tumor detection and it outperforms the other state-of-the-art methods on standard quality measures such as accuracy (97.18%), sensitivity (97%) and specificity (98.66%). The results reported in this paper are encouraging; hence, these results will motivate other researchers to explore more in this direction.

在本文中，我们提出了一种混合生物启发算法，该算法结合了鲸鱼优化算法（WOA）和自适应粒子群优化（APSO）的优点。提出的算法称为混合WOA_APSO算法。我们利用卷积神经网络（CNN）进行分类。进行了广泛的实验以评估所提出模型的性能。在此，对120幅肺部CT图像进行预处理和分割，以获得分割的肿瘤和非肿瘤区域结节。使用不同的技术从处理后的图像中提取统计，纹理，几何和结构特征。优化的特征选择在确定分类算法的准确性中起着至关重要的作用。提出了鲸鱼优化算法和自适应粒子群优化的新型变体，混合生物启发式WOA_APSO，以选择优化特征。通过嵌入线性判别分析来应用特征选择分组，这有助于确定子集的缩减维。进行了双重性能比较。首先，我们将性能与支持向量机，人工神经网络（ANN）和CNN等不同分类技术进行比较。其次，将混合WOA_APSO的计算成本与标准WOA和APSO算法进行比较。实验结果表明，该算法能够自动检测肺部肿瘤，并且在标准质量度量（例如准确度（97.18％））方面优于其他最新方法。敏感性（97％）和特异性（98.66％）。本文报道的结果令人鼓舞；因此，这些结果将激励其他研究人员朝这个方向进行更多探索。