Abunadi Ibrahim, Albraikan Amani Abdulrahman, Alzahrani Jaber S, Eltahir Majdy M, Hilal Anwer Mustafa, Eldesouki Mohamed I, Motwakel Abdelwahed, Yaseen Ishfaq
Department of Information Systems, College of Computer and Information Sciences, Prince Sultan University, Riyadh 12435, Saudi Arabia.
Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 11671, Saudi Arabia.
Healthcare (Basel). 2022 Apr 8;10(4):697. doi: 10.3390/healthcare10040697.
Recently, the COVID-19 epidemic has had a major impact on day-to-day life of people all over the globe, and it demands various kinds of screening tests to detect the coronavirus. Conversely, the development of deep learning (DL) models combined with radiological images is useful for accurate detection and classification. DL models are full of hyperparameters, and identifying the optimal parameter configuration in such a high dimensional space is not a trivial challenge. Since the procedure of setting the hyperparameters requires expertise and extensive trial and error, metaheuristic algorithms can be employed. With this motivation, this paper presents an automated glowworm swarm optimization (GSO) with an inception-based deep convolutional neural network (IDCNN) for COVID-19 diagnosis and classification, called the GSO-IDCNN model. The presented model involves a Gaussian smoothening filter (GSF) to eradicate the noise that exists from the radiological images. Additionally, the IDCNN-based feature extractor is utilized, which makes use of the Inception v4 model. To further enhance the performance of the IDCNN technique, the hyperparameters are optimally tuned using the GSO algorithm. Lastly, an adaptive neuro-fuzzy classifier (ANFC) is used for classifying the existence of COVID-19. The design of the GSO algorithm with the ANFC model for COVID-19 diagnosis shows the novelty of the work. For experimental validation, a series of simulations were performed on benchmark radiological imaging databases to highlight the superior outcome of the GSO-IDCNN technique. The experimental values pointed out that the GSO-IDCNN methodology has demonstrated a proficient outcome by offering a maximal sensy of 0.9422, specy of 0.9466, precn of 0.9494, accy of 0.9429, and F1score of 0.9394.
最近，新冠疫情对全球人民的日常生活产生了重大影响，需要各种筛查测试来检测冠状病毒。相反，结合放射图像的深度学习（DL）模型的开发有助于进行准确的检测和分类。DL模型充满了超参数，在如此高维的空间中识别最优参数配置并非易事。由于设置超参数的过程需要专业知识和大量反复试验，因此可以采用元启发式算法。出于这个动机，本文提出了一种基于inception的深度卷积神经网络（IDCNN）的自动萤火虫群优化（GSO）算法用于新冠诊断和分类，称为GSO-IDCNN模型。所提出的模型包括一个高斯平滑滤波器（GSF）以消除放射图像中存在的噪声。此外，还利用了基于IDCNN的特征提取器，它使用了Inception v4模型。为了进一步提高IDCNN技术的性能，使用GSO算法对超参数进行了优化调整。最后，使用自适应神经模糊分类器（ANFC）对新冠的存在情况进行分类。用于新冠诊断的带有ANFC模型的GSO算法设计体现了这项工作的新颖性。为了进行实验验证，在基准放射成像数据库上进行了一系列模拟，以突出GSO-IDCNN技术的卓越成果。实验值指出，GSO-IDCNN方法通过提供最大灵敏度0.9422、特异性0.9466、精确率0.9494、准确率0.9429和F1分数0.9394，展示了出色的结果。
