MH-COVIDNet: Diagnosis of COVID-19 using Deep Neural Networks and Meta-heuristic

MH-COVIDNet: Diagnosis of COVID-19 using Deep Neural Networks and Meta-heuristic-based Feature Selection on X-ray Images

COVID-19 is a disease that causes symptoms in the lungs and causes deaths around the world. Studies are ongoing for the diagnosis and treatment of this disease, which is defined as a pandemic.Early diagnosis of this disease is important for human life. This process is progressing rapidly with diagnostic studies based on deep learning. Therefore, to contribute to this field, a deep learning-based approach that can be used for early diagnosis of the disease is proposed in our study. In this approach, a data set consisting of 3 classes of COVID19, normal and pneumonia lung X-ray images was created, with each class containing 364 images. Pre-processing was performed using the image contrast enhancement algorithm on the prepared data set and a new data set was obtained. Feature extraction was completed from this data set with deep learning models such as AlexNet, VGG19, GoogleNet, and ResNet. For the selection of the best potential features, two metaheuristic algorithms of binary particle swarm optimization and binary gray wolf optimization were used. After combining the features obtained in the feature selection of the enhancement data set, they were classified using SVM. The overall accuracy of the proposed approach was obtained as 99.38%. The results obtained by verification with two different metaheuristic algorithms proved that the approach we propose can help experts during COVID-19 diagnostic studies.

MH-COVIDNet：使用深度神经网络和基于元启发式的X射线图像特征选择诊断COVID-19

COVID-19是一种会引起肺部症状并导致世界各地死亡的疾病。正在对该疾病的诊断和治疗进行研究，该疾病被定义为大流行病。.. 对该疾病的早期诊断对人类生命至关重要。通过基于深度学习的诊断研究，该过程正在迅速发展。因此，为促进该领域的发展，我们的研究提出了一种可用于疾病早期诊断的基于深度学习的方法。通过这种方法，创建了由3类COVID19，正常肺炎和肺炎X射线图像组成的数据集，每类包含364张图像。使用图像对比度增强算法对准备的数据集执行预处理，并获得新的数据集。使用深度学习模型（例如AlexNet，VGG19，GoogleNet和ResNet）从该数据集完成特征提取。为了选择最佳的潜在功能，使用了二元启发式算法的二进制粒子群算法和二进制灰狼算法。合并在增强数据集的特征选择中获得的特征后，使用SVM对它们进行分类。所提方法的整体准确性为99.38％。通过使用两种不同的元启发式算法进行验证所获得的结果证明，我们提出的方法可以在COVID-19诊断研究期间为专家提供帮助。 （阅读更多）