What is it about?

•A new method of training a deep convolutional neural network with layer truncation, transfer learning, partial layer freezing, and feature fusion was proposed. •Achieved a competitive performance with significantly lesser parameters than other state-of-the-art models that diagnosed chest x-rays with COVID-19. •The proposed method and model can scale less effortlessly and does not consume massive amounts of disk capacities even with an increased amount of data.

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Why is it important?

This work proposed the Fused-DenseNet-Tiny, a lightweight DCNN model based on a densely connected neural network (DenseNet) truncated and concatenated. The model trained to learn CXR features based on transfer learning, partial layer freezing, and feature fusion. Upon evaluation, the proposed model achieved a remarkable 97.99 % accuracy, with only 1.2 million parameters and a shorter end-to-end structure. It has also shown better performance than some existing studies and other massive state-of-the-art models that diagnosed COVID-19 from CXRs.

Perspectives

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.

Dr. Francis Perez Montalbo
Batangas State University

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This page is a summary of: Diagnosing Covid-19 chest x-rays with a lightweight truncated DenseNet with partial layer freezing and feature fusion, Biomedical Signal Processing and Control, July 2021, Elsevier, DOI: 10.1016/j.bspc.2021.102583.
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