Mittal Himanshu, Pandey Avinash Chandra, Pal Raju, Tripathi Ashish
Jaypee Institute of Information Technology, Noida, India.
Malaviya National Institute of Technology, Jaipur, India.
Appl Intell (Dordr). 2021;51(5):2988-3011. doi: 10.1007/s10489-020-02122-3. Epub 2021 Jan 23.
With the spread of COVID-19, there is an urgent need for a fast and reliable diagnostic aid. For the same, literature has witnessed that medical imaging plays a vital role, and tools using supervised methods have promising results. However, the limited size of medical images for diagnosis of CoVID19 may impact the generalization of such supervised methods. To alleviate this, a new clustering method is presented. In this method, a novel variant of a gravitational search algorithm is employed for obtaining optimal clusters. To validate the performance of the proposed variant, a comparative analysis among recent metaheuristic algorithms is conducted. The experimental study includes two sets of benchmark functions, namely standard functions and CEC2013 functions, belonging to different categories such as unimodal, multimodal, and unconstrained optimization functions. The performance comparison is evaluated and statistically validated in terms of mean fitness value, Friedman test, and box-plot. Further, the presented clustering method tested against three different types of publicly available CoVID19 medical images, namely X-ray, CT scan, and Ultrasound images. Experiments demonstrate that the proposed method is comparatively outperforming in terms of accuracy, precision, sensitivity, specificity, and F1-score.
随着新冠病毒（COVID-19）的传播，迫切需要一种快速且可靠的诊断辅助手段。为此，文献表明医学成像发挥着至关重要的作用，且使用监督方法的工具取得了有前景的成果。然而，用于诊断COVID-19的医学图像数量有限可能会影响此类监督方法的泛化能力。为缓解这一问题，提出了一种新的聚类方法。在该方法中，采用了一种引力搜索算法的新颖变体来获取最优聚类。为验证所提变体的性能，对近期的元启发式算法进行了对比分析。实验研究包括两组基准函数，即标准函数和CEC2013函数，它们属于不同类别，如单峰、多峰和无约束优化函数。性能比较通过平均适应度值、Friedman检验和箱线图进行评估并得到统计验证。此外，所提出的聚类方法针对三种不同类型的公开可用COVID-19医学图像进行了测试，即X射线、CT扫描和超声图像。实验表明，所提方法在准确性、精确性、敏感性、特异性和F1分数方面表现相对更优。
