Department of Computer Science and Engineering, Qatar University, Doha, Qatar.
Sci Rep. 2021 Aug 27;11(1):17318. doi: 10.1038/s41598-021-96601-3.
Among the most leading causes of mortality across the globe are infectious diseases which have cost tremendous lives with the latest being coronavirus (COVID-19) that has become the most recent challenging issue. The extreme nature of this infectious virus and its ability to spread without control has made it mandatory to find an efficient auto-diagnosis system to assist the people who work in touch with the patients. As fuzzy logic is considered a powerful technique for modeling vagueness in medical practice, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was proposed in this paper as a key rule for automatic COVID-19 detection from chest X-ray images based on the characteristics derived by texture analysis using gray level co-occurrence matrix (GLCM) technique. Unlike the proposed method, especially deep learning-based approaches, the proposed ANFIS-based method can work on small datasets. The results were promising performance accuracy, and compared with the other state-of-the-art techniques, the proposed method gives the same performance as the deep learning with complex architectures using many backbone.
在全球范围内，导致死亡率最高的原因之一是传染病，其中最新的是冠状病毒（COVID-19），它已成为最近最具挑战性的问题。这种具有极强传染性的病毒的性质及其失控传播的能力，使得必须找到一种有效的自动诊断系统来帮助与患者接触的人。由于模糊逻辑被认为是在医学实践中建模模糊性的一种强大技术，因此本文提出了一种自适应神经模糊推理系统（ANFIS）作为基于纹理分析的自动 COVID-19 检测的关键规则，该方法使用灰度共生矩阵（GLCM）技术提取特征。与所提出的方法（特别是基于深度学习的方法）不同，所提出的基于 ANFIS 的方法可以在小数据集上运行。结果表明，该方法具有良好的性能和准确性，与其他最先进的技术相比，该方法在使用许多骨干网络的复杂架构的深度学习方面具有相同的性能。
