What is it about?
The highlights of the proposed work. •This novel method significantly reduced the model's parameter size without sacrificing much of its classification performance. •The proposed method had better performance against some state-of-the-art Deep Convolutional Neural Network (DCNN) models that diagnosed samples of CXRs with COVID-19. •The proposed method delivered a conveniently scalable, reproducible, and deployable DCNN model for most low-end devices.
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Why is it important?
The proposed method was employed on a Densely Connected Convolutional Neural Network (CNN), the DenseNet model, to diagnose whether a Chest X-Ray (CXR) is well, has Pneumonia, or has COVID-19. From the results, the performance to parameter size ratio highlighted this method's effectiveness to train a DenseNet model with fewer parameters compared to traditionally trained state-of-the-art Deep CNN (DCNN) models, yet yield promising results.
Read the Original
This page is a summary of: Truncating a densely connected convolutional neural network with partial layer freezing and feature fusion for diagnosing COVID-19 from chest X-rays, MethodsX, January 2021, Elsevier, DOI: 10.1016/j.mex.2021.101408.
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