TinyCov-NET: Efficient and robust COVID-19 pneumonia diagnosis using a stacked shallow convolutional neural network

To identify infections caused by the COVID-19 virus, specifically those leading to Pneumonia. The datasets used included images of infected individuals, X-rays of the Chest, and standard non-COVID-19 X-ray images of the chest.

In our research, X-ray chest images were utilized to detect Pneumonia caused by COVID-19. The dataset was collected from the medical database of Johns Hopkins University (USA). To extract detailed features and characteristics from the input images and aid in detecting COVID-19 induced pneumonia cases, a lightweight Stacked Shallow Convolutional Neural Network (CNN) was implemented in the proposed work.

An overall sample size of 2292 input images was considered, with 542 COVID-19 infected images and 1266 non-COVID-19 images used for training. Similarly, for testing, 167 COVID-19 infected images and 317 non-COVID-19 infected images were used. Our proposed model was validated against an established Stacked Shallow CNN to analyze its accuracy. It demonstrated that the proposed framework achieved an accuracy of 98.76%, with each class accuracy of 98.42% for diagnosing standard cases and 99.40% for diagnosing infected COVID-19 cases.

In light of the ongoing research in enhancing the Deep Neural Network model (DNN) to accomplish better accuracy, the CNN architecture presented in this paper introduces a Stacked Shallow Learning approach. This leads to a significant enhancement in both accuracy and efficiency, particularly in computation and response time. Our work outlines a lightweight architecture that is instrumental in expediting the diagnosis of COVID-19 by streamlining response times effectively. Our proposed model also has limitations, such that it cannot hold a large-scale unbalanced dataset and has less scope for pre-processing the data set. Furthermore, the suggested model cannot capture the lung region impacted by the COVID-19 infection.