The novel coronavirus termed as covid-19 has taken the world by its crutches affecting innumerable lives with devastating impact on the global economy and public health. One of the major ways to control the spread of this disease is identification in the initial stage, so that isolation and treatment could be initiated. Due to the lack of automated auxiliary diagnostic medical tools, availability of lesser sensitivity testing kits, and limited availability of healthcare professionals, the pandemic has spread like wildfire across the world. Certain recent findings state that chest X-ray scans contain salient information regarding the onset of the virus, the information can be analyzed so that the diagnosis and treatment can be initiated at an earlier stage. This is where artificial intelligence meets the diagnostic capabilities of experienced clinicians. The objective of the proposed research is to contribute towards fighting the global pandemic by developing an automated image analysis module for identifying covid-19 affected chest X-ray scans by employing an optimized Convolution Neural Network (CNN) model. The aforementioned objective is achieved in the following manner by developing three classification models, (i) ensemble of ResNet 50-Error Correcting Output Code (ECOC) model, (ii) CNN optimized using Grey Wolf Optimizer (GWO) and, (iii) CNN optimized using Whale Optimization + BAT algorithm. The novelty of the proposed method lies in the automatic tuning of hyper parameters considering a hierarchy of MultiLayer Perceptron (MLP), feature extraction, and optimization ensemble. A 100% classification accuracy was obtained in classifying covid-19 images. Classification accuracy of 98.8% and 96% were obtained for dataset 1 and dataset 2 respectively for classification into covid-19, normal, and viral pneumonia cases. The proposed method can be adopted in a clinical setting for assisting radiologists and it can also be employed in remote areas to facilitate the faster screening of affected patients.

被称为 covid-19 的新型冠状病毒已经席卷了世界，影响了无数人的生命，对全球经济和公共卫生产生了毁灭性影响。控制这种疾病传播的主要方法之一是在初始阶段进行识别，以便启动隔离和治疗。由于缺乏自动化辅助诊断医疗工具、灵敏度较低的检测试剂盒的可用性以及医疗保健专业人员的有限，这一流行病已像野火一样在世界各地蔓延。最近的某些发现表明，胸部 X 光扫描包含有关病毒发作的重要信息，可以对这些信息进行分析，以便在早期阶段开始诊断和治疗。这就是人工智能与经验丰富的临床医生的诊断能力相结合的地方。拟议研究的目的是通过开发自动图像分析模块，通过采用优化的卷积神经网络 (CNN) 模型来识别受 covid-19 影响的胸部 X 射线扫描，为抗击全球大流行做出贡献。上述目标是通过开发三种分类模型以下列方式实现的：(i) ResNet 50-Error Correcting Output Code (ECOC) 模型的集成，(ii) 使用灰狼优化器 (GWO) 优化的 CNN，以及 (iii) CNN采用鲸鱼优化+BAT算法进行优化。该方法的新颖性在于考虑多层感知器（MLP）的层次结构、特征提取和优化集成来自动调整超参数。对 covid-19 图像进行分类时获得了 100% 的分类准确率。分类准确度98。数据集 1 和数据集 2 分别获得了 8% 和 96% 的分类结果，用于分类 covid-19、正常和病毒性肺炎病例。所提出的方法可以在临床环境中采用，以协助放射科医生，也可以在偏远地区采用，以促进对受影响患者的更快筛查。