Covid-19 has become a deadly pandemic claiming more than three million lives worldwide. SARS-CoV-2 causes distinct pathomorphological alterations in the respiratory system, thereby acting as a biomarker to aid its diagnosis. A multimodal framework (Ai-CovScan) for Covid-19 detection using breathing sounds, chest X-ray (CXR) images, and rapid antigen test (RAnT) is proposed. Transfer Learning approach using existing deep-learning Convolutional Neural Network (CNN) based on Inception-v3 is combined with Multi-Layered Perceptron (MLP) to develop the CovScanNet model for reducing false-negatives. This model reports a preliminary accuracy of 80% for the breathing sound analysis, and 99.66% Covid-19 detection accuracy for the curated CXR image dataset. Based on Ai-CovScan, a smartphone app is conceptualised as a mass-deployable screening tool, which could alter the course of this pandemic. This app’s deployment could minimise the number of people accessing the limited and expensive confirmatory tests, thereby reducing the burden on the severely stressed healthcare infrastructure.

Covid-19 已成为一种致命的流行病，在全球范围内夺走了超过 300 万人的生命。SARS-CoV-2 会在呼吸系统中引起明显的病理形态学改变，从而作为一种生物标志物来帮助其诊断。提出了一种使用呼吸音、胸部 X 光 (CXR) 图像和快速抗原检测 (RAnT) 检测 Covid-19 的多模式框架 (Ai-CovScan)。使用基于 Inception-v3 的现有深度学习卷积神经网络 (CNN) 的迁移学习方法与多层感知器 (MLP) 相结合，以开发用于减少假阴性的 CovScanNet 模型。该模型报告呼吸音分析的初步准确度为 80%，精选的 CXR 图像数据集的 Covid-19 检测准确度为 99.66%。基于智能手机应用程序Ai-CovScan被概念化为一种可大规模部署的筛选工具，它可能会改变这种流行病的进程。该应用程序的部署可以最大限度地减少访问有限且昂贵的确认测试的人数，从而减轻压力严重的医疗保健基础设施的负担。