Ashour Amira S, Eissa Merihan M, Wahba Maram A, Elsawy Radwa A, Elgnainy Hamada Fathy, Tolba Mohamed Saeed, Mohamed Waleed S
Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Egypt.
Department of Electronics and Communication Engineering, Alexandria Higher Institute of Engineering &Technology, Egypt.
Biomed Signal Process Control. 2021 Jul;68:102656. doi: 10.1016/j.bspc.2021.102656. Epub 2021 Apr 20.
The medical and scientific communities are currently trying to treat infected patients and develop vaccines for preventing a future outbreak. In healthcare, machine learning is proven to be an efficient technology for helping to combat the COVID-19. Hospitals are now overwhelmed with the increased infections of COVID-19 cases and given patients' confidentiality and rights. It becomes hard to assemble quality medical image datasets in a timely manner. For COVID-19 diagnosis, several traditional computer-aided detection systems based on classification techniques were proposed. The bag-of-features (BoF) model has shown a promising potential in this domain. Thus, this work developed an ensemble-based BoF classification system for the COVID-19 detection. In this model, we proposed ensemble at the classification step of the BoF. The proposed system was evaluated and compared to different classification systems for different number of visual words to evaluate their effect on the classification efficiency. The results proved the superiority of the proposed ensemble-based BoF for the classification of normal and COVID19 chest X-ray (CXR) images compared to other classifiers.
医学和科学界目前正在努力治疗受感染患者，并研发预防未来疫情爆发的疫苗。在医疗保健领域，机器学习已被证明是一种有助于抗击新冠疫情的有效技术。由于新冠病例感染人数增加，医院不堪重负，同时还要考虑患者的隐私和权利。因此，及时收集高质量的医学图像数据集变得很困难。对于新冠诊断，人们提出了几种基于分类技术的传统计算机辅助检测系统。特征袋（BoF）模型在该领域已显示出有前景的潜力。因此，这项工作开发了一种基于集成的BoF分类系统用于新冠检测。在这个模型中，我们在BoF的分类步骤提出了集成方法。对所提出的系统进行了评估，并与针对不同数量视觉词的不同分类系统进行比较，以评估它们对分类效率的影响。结果证明，与其他分类器相比，所提出的基于集成的BoF在正常和新冠胸部X光（CXR）图像分类方面具有优越性。
