A Lightweight Diagnostic tool for COVID-19 chest X-rays with AI-capability

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Due to the relentless spread of COVID-19 infections, mass testing became an essential aspect of most people's lives today. However, the gold standard testing procedure like rRT-PCR requires specialized testing equipment and a trained medical practitioner. Even with a lesser substitute like CXRs, a rapid and less expensive method to rule out the infections of COVID-19 still induces complexity in most underdeveloped countries. Hence, people started to automate such a difficult task through DL. This work served as an additional contribution with its lightweight yet efficient design that requires less effort to reproduce and does not require expensive equipment to help diagnose COVID-19 infections from CXRs. As a result, the proposed model yielded a slight performance improvement but a massive decrease in computing cost and parameter size over other state-of-the-art models and existing studies. However, even with its lightweight design, a specific caveat still shows that the Fused-DenseNet-Tiny cannot outperform its larger counterpart due to its reduced extraction capabilities. Though not far in terms of performance, this work hypothesizes that hyper-parameter optimization, and the potential addition of more data may yield additional improvements that can alleviate such a problem in the future. Nonetheless, even with minimal shortcomings, this work still concludes that the fusion of a mirrored truncated DenseNet, with its equivalent partially trained with ImageNet features and the other with shared weights from the CXR and ImageNet, the proposed model still performed efficiently even with less computing cost, data, and dependence to other sophisticated optimization methods towards the diagnosis of COVID-19 from CXRs. Furthermore, the model can still induce further improvements upon applying the mentioned methods above, besides adding more data.

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