COVID-19 Detection from Chest X-Ray Images Using Deep Convolutional Neural Networks with Weights Imprinting Approach

Abstract

COVID-19 pandemic has drastically changed our lives. Chest radiography
has been used to detect COVID-19. However, the number
of publicly available COVID-19 x-ray images is extremely limited,
resulting in a highly imbalanced dataset. This is a challenge when
using deep learning for classification and detection. In this work, we
propose the use of pre-trained deep Convolutional Neural Networks
(CNN) and integrate them with a few-shot learning approach named
imprinted weights. The integrated model is fine tuned to enhance
the capability of detecting COVID-19. The proposed solution then
combines the fine-tuned models using a weighted average ensemble
for achieving an optimal 82% sensitivity to COVID-19. To the
best of authors’ knowledge, the proposed solution is one of the first
to utilize imprinted weights model with weighted average ensemble
for enhancing the model sensitivity to COVID-19.