A novel lightweight deep learning model based on SqueezeNet architecture for viral lung disease classification in X-ray and CT images - Agnihotri, Abhishek; Kohli, Narendra (International Journal of Computational and Experimental Science and Engineering, 2024)
COVID-19 has affected hundreds of millions of individuals, seriously harming the global population’s health, welfare, and economy. Furthermore, health facilities are severely overburdened due to the record number of COVID-19 cases, which makes prompt and accurate diagnosis difficult. Automatically identifying infected individuals and promptly placing them under special care is a critical step in reducing the burden of such issues. Convolutional Neural Networks (CNN) and other machine learning techniques can be utilized to address this demand.  Many existing Deep learning models, albeit producing the intended outcomes, were developed using millions of parameters, making them unsuitable for use on devices with constrained resources. Motivated by this fact, a novel lightweight deep learning model based on Efficient Channel Attention (ECA) module and SqueezeNet architecture, is developed in this work to identify COVID-19 patients from chest X-ray and CT images in the initial phases of the disease. After the proposed lightweight model was tested on different datasets with two, three and four classes, the results show its better performance over existing models. The outcomes shown that, in comparison to the current heavyweight models, our models reduced the cost and memory requirements for computing resources dramatically, while still achieving comparable performance. These results support the notion that proposed model can help diagnose Covid-19 in patients by being easily implemented on low-resource and low-processing devices.