Nowadays, COVID severity prediction has attracted widely in medical research because of the disease severity. Hence, the image processing application is also utilized to analyze COVID severity identification using lungs X-ray images. Thus, several intelligent schemes were employed to detect the COVID-affected part of the lungs X-ray images. However, the traditional neural approaches reported less severity classification accuracy due to the image complexity score. So, the present study has presented a novel chimp-based Adaboost Severity Analysis (CbASA) implemented in the MATLAB environment. Hence, the lung's X-ray images are utilized to test the working performance of the designed model. All public imaging data sources contain more noisy features, so the noise features are removed in the initial hidden layer of the novel CbASA then the noise-free data is imported into the classification phase. Feature extraction, segmentation, and severity specification have been performed in the classification layer. Finally, the performance of the classification score has been measured and compared with other models. Subsequently, the presented novel CbASA has earned the finest classification outcome.

如今，新冠肺炎严重程度预测因其疾病的严重程度而在医学研究中受到广泛关注。因此，图像处理应用程序还用于使用肺部 X 射线图像来分析新冠肺炎严重程度识别。因此，采用了几种智能方案来检测肺部 X 射线图像中受新冠病毒影响的部分。然而，由于图像复杂性得分，传统的神经方法报告的严重性分类准确性较低。因此，本研究提出了一种在 MATLAB 环境中实现的新型基于黑猩猩的 Adaboost 严重性分析 (CbASA)。因此，利用肺部的 X 射线图像来测试所设计模型的工作性能。所有公共成像数据源都包含更多噪声特征，因此在新型 CbASA 的初始隐藏层中去除噪声特征，然后将无噪声数据导入分类阶段。特征提取、分割和严重性规范已在分类层中执行。最后，测量了分类分数的性能并与其他模型进行了比较。随后，所提出的新颖的 CbASA 获得了最好的分类结果。