The proposed method offers very satisfactory performances compared to the state of the art methods and achieves an accuracy of 90:13% on the 4-class, 96:45% on a 3-class, and 99:39% on 2-class classification. An end to end training is performed for datasets containing classes: COVID-19, Normal, Bacterial Pneumonia, Viral Pneumonia.
Considering the final classification task, an EfficientNet-B4 network is utilized in both stages. Finally, the encoder-merging network is trained for feature extraction of the X-ray images in a supervised manner and resulting features are used in the classification layers of the proposed architecture. An intelligent feature merging scheme is introduced in the proposed merging network. Here the encoder model is initialized with the weights learned on the first stage and the outputs from different layers of the encoder model are used effectively by being connected to a proposed merging network. An encoder-merging network is proposed for the second stage that consists of different layers of the encoder model followed by a merging network. In the first stage, an encoder-decoder based autoencoder network is proposed, trained on chest X-ray images in an unsupervised manner, and the network learns to reconstruct the X-ray images. In this paper, a two-stage deep CNN based scheme is proposed to detect COVID-19 from chest X-ray images for achieving optimum performance with limited training images. However, the limitation of the labeled data is the main bottleneck of the data-hungry deep learning methods. Deep learning-based approaches have been established as the most promising methods in this regard. With the onset of the COVID-19 pandemic, the automated diagnosis has become one of the most trending topics of research for faster mass screening.
0 kommentar(er)
