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Abstract：
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.
摘要：
随着COVID-19的传播，迫切需要一种快速可靠的诊断辅助手段。同样，文献见证了医学成像起着至关重要的作用，和使用监督方法的工具有希望的结果。然而,用于CoVID19诊断的医学图像尺寸有限可能会影响此类监督方法的推广.为了缓解这种情况,提出了一种新的聚类方法。在这种方法中，引力搜索算法的一个新的变体被用来获得最优聚类。为了验证所提出的变体的性能，对最近的元启发式算法进行了比较分析。实验研究包括两组基准函数,即标准函数和CEC2013函数，属于不同的类别，如单峰，多模态,和无约束优化函数。根据平均适应度值对性能比较进行评估和统计验证，弗里德曼测试,和箱线图。Further,提出的聚类方法针对三种不同类型的公开可用的CoVID19医学图像进行了测试，也就是X光,CT扫描,和超声图像。实验表明，该方法在精度方面具有比较好的表现，精度,灵敏度,特异性，和F1得分。
