文献类型：期刊文献

英文题名：Lung adenocarcinoma identification based on hybrid feature selections and attentional convolutional neural networks

作者：Li, Kunpeng[1]; Wang, Zepeng[1]; Zhou, Yu[1]; Li, Sihai[1]

第一作者：Li, Kunpeng

机构：[1] School of Information Engineering, Gansu University of Chinese Medicine, Lanzhou, 730000, China

第一机构：甘肃中医药大学

通信机构：[1]School of Information Engineering, Gansu University of Chinese Medicine, Lanzhou, 730000, China|[10735]甘肃中医药大学;

年份：2024

卷号：21

期号：2

起止页码：2991

外文期刊名：Mathematical Biosciences and Engineering

收录：EI(收录号：20241816012043);Scopus(收录号：2-s2.0-85187491549)

语种：英文

外文关键词：Aspect ratio - Convolution - Convolutional neural networks - Deep learning - Feature Selection - Gene expression - K-means clustering - Particle size analysis - Particle swarm optimization (PSO) - Trees (mathematics)

摘要：Lung adenocarcinoma, a chronic non-small cell lung cancer, needs to be detected early. Tumor gene expression data analysis is effective for early detection, yet its challenges lie in a small sample size, high dimensionality, and multi-noise characteristics. In this study, we propose a lung adenocarcinoma convolutional neural network (LATCNN), a deep learning model tailored for accurate lung adenocarcinoma prediction and identification of key genes. During the feature selection stage, we introduce a hybrid algorithm. Initially, the fast correlation-based filter (FCBF) algorithm swiftly filters out irrelevant features, followed by applying the k-means-synthetic minority over-sampling technique (k-means-SMOTE) method to address category imbalance. Subsequently, we enhance the particle swarm optimization (PSO) algorithm by incorporating fast-decay dynamic inertia weights and utilizing the classification and regression tree (CART) as the fitness function for the second stage of feature selection, aiming to further eliminate redundant features. In the classifier construction stage, we present an attention convolutional neural network (atCNN) that incorporates an attention mechanism. This improved model conducts feature selection post lung adenocarcinoma gene expression data analysis for classification and prediction. The results show that LATCNN effectively reduces the feature dimensions and accurately identifies 12 key genes with accuracy, recall, F1 score, and MCC of 99.70%, 99.33%, 99.98%, and 98.67%, respectively. These performance metrics surpass those of other comparative models, highlighting the significance of this research for advancing lung adenocarcinoma treatment. ? 2024 American Institute of Mathematical Sciences. All rights reserved.