LMNet: Lightweight multi-scale convolutional neural network architecture for COVID-19 detection in IoMT environment

• Published in: Computers & electrical engineering 2022-10, Vol.103, p.108325-108325, Article 108325
• Main Authors: Dwivedy, Vishwajeet, Shukla, Harsh Deep, Roy, Pradeep Kumar
• Format: Article
• Language: English
• Subjects: Classification; Convolutional neural network; COVID-19; Deep learning; Ensemble learning; Transfer learning
• ISSN: 0045-7906; 1879-0755
• DOI: 10.1016/j.compeleceng.2022.108325

The COVID-19 disease, initially known as SARS-CoV-2, was first reported in early December 2019 and has caused immense damage to humans globally. The most widely used clinical screening method for COVID-19 is Reverse Transcription Polymerase Chain Reaction (RT-PCR). RT-PCR uses respiratory samples for testing, because of which, this manual technique becomes complicated, laborious and time-consuming. Even though it has a low sensitivity, it carries a considerable risk for the testing medical staff. Hence, there is a need for an automated diagnosis system that can provide quick and efficient diagnosis results. This research proposed a multi-scale lightweight CNN (LMNet) architecture for COVID-19 detection. The proposed model is computationally less expensive than previously available models and requires less memory space. The performance of the proposed LMNet model ensemble with DenseNet169 and MobileNetV2 is higher than the other state-of-the-art models. The ensemble model can be integrated at the backend of the smart devices; hence it is useful for the Internet of Medical Things (IoMT) environment. •A lightweight multi-scale CNN (LMNet) architecture is proposed for COVID-19 detection.•The proposed model is suitable for embedding in Internet-enabled devices.•The LMNet model has lesser parameters than experimented transfer learning models.•Performance comparison between the proposed model and existing models has been done. [Display omitted]