文章：

蜣螂优化与深度特征融合模型用于肺癌检测与分类

Dung Beetle Optimization with Deep Feature Fusion Model for Lung Cancer Detection and Classification

原文发布日期：5 August 2023

DOI: 10.3390/cancers15153982

类型: Article

开放获取: 是

英文摘要：

Lung cancer is the main cause of cancer deaths all over the world. An important reason for these deaths was late analysis and worse prediction. With the accelerated improvement of deep learning (DL) approaches, DL can be effectively and widely executed for several real-world applications in healthcare systems, like medical image interpretation and disease analysis. Medical imaging devices can be vital in primary-stage lung tumor analysis and the observation of lung tumors from the treatment. Many medical imaging modalities like computed tomography (CT), chest X-ray (CXR), molecular imaging, magnetic resonance imaging (MRI), and positron emission tomography (PET) systems are widely analyzed for lung cancer detection. This article presents a new dung beetle optimization modified deep feature fusion model for lung cancer detection and classification (DBOMDFF-LCC) technique. The presented DBOMDFF-LCC technique mainly depends upon the feature fusion and hyperparameter tuning process. To accomplish this, the DBOMDFF-LCC technique uses a feature fusion process comprising three DL models, namely residual network (ResNet), densely connected network (DenseNet), and Inception-ResNet-v2. Furthermore, the DBO approach was employed for the optimum hyperparameter selection of three DL approaches. For lung cancer detection purposes, the DBOMDFF-LCC system utilizes a long short-term memory (LSTM) approach. The simulation result analysis of the DBOMDFF-LCC technique of the medical dataset is investigated using different evaluation metrics. The extensive comparative results highlighted the betterment of the DBOMDFF-LCC technique of lung cancer classification.

摘要翻译： 

肺癌是全球癌症死亡的主要原因。这些死亡的一个重要原因是诊断过晚及预后不良。随着深度学习方法的加速发展，深度学习能够有效且广泛地应用于医疗健康系统的多个实际场景，如医学图像解读与疾病分析。医学影像设备在早期肺肿瘤分析及治疗过程中的肺肿瘤监测方面具有至关重要的作用。目前，多种医学影像模态如计算机断层扫描（CT）、胸部X光（CXR）、分子成像、磁共振成像（MRI）以及正电子发射断层扫描（PET）系统已被广泛应用于肺癌检测。本文提出了一种新型的基于蜣螂优化改进的深度特征融合模型，用于肺癌检测与分类（DBOMDFF-LCC）技术。该DBOMDFF-LCC技术主要依赖于特征融合与超参数调优过程。为此，DBOMDFF-LCC技术采用了包含三个深度学习模型的特征融合流程，即残差网络（ResNet）、密集连接网络（DenseNet）和Inception-ResNet-v2。此外，利用蜣螂优化方法对三种深度学习模型的超参数进行优选。在肺癌检测方面，DBOMDFF-LCC系统采用了长短期记忆网络方法。通过多种评估指标对医学数据集上DBOMDFF-LCC技术的仿真结果进行了分析。广泛的对比结果凸显了DBOMDFF-LCC技术在肺癌分类方面的优越性。

原文链接：

Dung Beetle Optimization with Deep Feature Fusion Model for Lung Cancer Detection and Classification