El-Kenawy El-Sayed M, Mirjalili Seyedali, Ibrahim Abdelhameed, Alrahmawy Mohammed, El-Said M, Zaki Rokaia M, Eid Marwa Metwally
Department of Communications and ElectronicsDelta Higher Institute of Engineering and Technology (DHIET) Mansoura 35111 Egypt.
Centre for Artificial Intelligence Research and OptimizationTorrens University Australia Fortitude Valley QLD 4006 Australia.
IEEE Access. 2021 Feb 22;9:36019-36037. doi: 10.1109/ACCESS.2021.3061058. eCollection 2021.
The chest X-ray is considered a significant clinical utility for basic examination and diagnosis. The human lung area can be affected by various infections, such as bacteria and viruses, leading to pneumonia. Efficient and reliable classification method facilities the diagnosis of such infections. Deep transfer learning has been introduced for pneumonia detection from chest X-rays in different models. However, there is still a need for further improvements in the feature extraction and advanced classification stages. This paper proposes a classification method with two stages to classify different cases from the chest X-ray images based on a proposed Advanced Squirrel Search Optimization Algorithm (ASSOA). The first stage is the feature learning and extraction processes based on a Convolutional Neural Network (CNN) model named ResNet-50 with image augmentation and dropout processes. The ASSOA algorithm is then applied to the extracted features for the feature selection process. Finally, the Multi-layer Perceptron (MLP) Neural Network's connection weights are optimized by the proposed ASSOA algorithm (using the selected features) to classify input cases. A Kaggle chest X-ray images (Pneumonia) dataset consists of 5,863 X-rays is employed in the experiments. The proposed ASSOA algorithm is compared with the basic Squirrel Search (SS) optimization algorithm, Grey Wolf Optimizer (GWO), and Genetic Algorithm (GA) for feature selection to validate its efficiency. The proposed (ASSOA + MLP) is also compared with other classifiers, based on (SS + MLP), (GWO + MLP), and (GA + MLP), in performance metrics. The proposed (ASSOA + MLP) algorithm achieved a classification mean accuracy of (99.26%). The ASSOA + MLP algorithm also achieved a classification mean accuracy of (99.7%) for a chest X-ray COVID-19 dataset tested from GitHub. The results and statistical tests demonstrate the high effectiveness of the proposed method in determining the infected cases.
胸部X光被认为在基础检查和诊断中具有重要的临床应用价值。人类肺部区域可能受到各种感染，如细菌和病毒感染，从而引发肺炎。高效可靠的分类方法有助于此类感染的诊断。不同模型已引入深度迁移学习用于从胸部X光片中检测肺炎。然而，在特征提取和高级分类阶段仍需进一步改进。本文提出一种两阶段分类方法，基于一种改进的松鼠搜索优化算法（ASSOA）对胸部X光图像中的不同病例进行分类。第一阶段是基于名为ResNet - 50的卷积神经网络（CNN）模型进行特征学习和提取过程，同时进行图像增强和随机失活处理。然后将ASSOA算法应用于提取的特征进行特征选择过程。最后，通过所提出的ASSOA算法（使用所选特征）优化多层感知器（MLP）神经网络的连接权重来对输入病例进行分类。实验采用了一个包含5863张X光片的Kaggle胸部X光图像（肺炎）数据集。将所提出的ASSOA算法与基本松鼠搜索（SS）优化算法、灰狼优化器（GWO）和遗传算法（GA）进行特征选择比较，以验证其效率。在性能指标方面，所提出的（ASSOA + MLP）方法也与基于（SS + MLP）、（GWO + MLP）和（GA + MLP）的其他分类器进行了比较。所提出的（ASSOA + MLP）算法实现了99.26%的分类平均准确率。对于从GitHub测试的胸部X光COVID - 19数据集，ASSOA + MLP算法还实现了99.7%的分类平均准确率。结果和统计测试表明所提出的方法在确定感染病例方面具有很高的有效性。
