Department of Electrical Engineering, Jadavpur University, Kolkata, 700032, India.
Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032, India.
Comput Biol Med. 2021 Aug;135:104585. doi: 10.1016/j.compbiomed.2021.104585. Epub 2021 Jun 22.
The COVID-19 outbreak has resulted in a global pandemic and led to more than a million deaths to date. COVID-19 early detection is essential for its mitigation by controlling its spread from infected patients in communities through quarantine. Although vaccination has started, it will take time to reach everyone, especially in developing nations, and computer scientists are striving to come up with competent methods using image analysis. In this work, a classifier ensemble technique is proposed, utilizing Choquet fuzzy integral, wherein convolutional neural network (CNN) based models are used as base classifiers. It classifies chest X-ray images from patients with common Pneumonia, confirmed COVID-19, and healthy lungs. Since there are few samples of COVID-19 cases for training on a standard CNN model from scratch, we use the transfer learning scheme to train the base classifiers, which are InceptionV3, DenseNet121, and VGG19. We utilize the pre-trained CNN models to extract features and classify the chest X-ray images using two dense layers and one softmax layer. After that, we combine the prediction scores of the data from individual models using Choquet fuzzy integral to get the final predicted labels, which is more accurate than the prediction by the individual models. To determine the fuzzy-membership values of each classifier for the application of Choquet fuzzy integral, we use the validation accuracy of each classifier. The proposed method is evaluated on chest X-ray images in publicly available repositories (IEEE and Kaggle datasets). It provides 99.00%, 99.00%, 99.00%, and 99.02% average recall, precision, F-score, and accuracy, respectively. We have also evaluated the performance of the proposed model on an inter-dataset experimental setup, where chest X-ray images from another dataset (CMSC-678-ML-Project GitHub dataset) are fed to our trained model and we have achieved 99.05% test accuracy on this dataset. The results are better than commonly used classifier ensemble methods as well as many state-of-the-art methods.
新型冠状病毒肺炎疫情已在全球蔓延，导致截至目前已有超过 100 万人死亡。通过对社区中受感染者进行隔离以控制其传播，可以有效减轻新型冠状病毒肺炎疫情。虽然疫苗接种已经开始，但要覆盖所有人还需要时间，特别是在发展中国家。计算机科学家正在努力开发使用图像分析的有效方法。在这项工作中，提出了一种分类器集成技术，利用 Choquet 模糊积分，其中使用基于卷积神经网络（CNN）的模型作为基分类器。它可以对患有普通肺炎、确诊的新型冠状病毒肺炎和健康肺部的患者的胸部 X 光图像进行分类。由于从头开始训练标准 CNN 模型的新型冠状病毒肺炎病例样本较少，因此我们使用迁移学习方案来训练基分类器，即 InceptionV3、DenseNet121 和 VGG19。我们利用预训练的 CNN 模型来提取特征，并使用两个密集层和一个 softmax 层对胸部 X 光图像进行分类。然后，我们使用 Choquet 模糊积分组合来自各个模型的数据的预测得分，以获得最终的预测标签，这比单个模型的预测更准确。为了确定 Choquet 模糊积分应用中每个分类器的模糊隶属度值，我们使用每个分类器的验证准确性。该方法在公开可用的存储库（IEEE 和 Kaggle 数据集）中对胸部 X 光图像进行了评估。它分别提供了 99.00%、99.00%、99.00%和 99.02%的平均召回率、精度、F 分数和准确率。我们还在数据集间的实验设置中评估了所提出模型的性能，即将另一个数据集（CMSC-678-ML-Project GitHub 数据集）的胸部 X 光图像输入到我们训练的模型中，我们在该数据集上获得了 99.05%的测试准确率。与常用的分类器集成方法以及许多最先进的方法相比，该结果更好。
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