Efficient intelligent compression and recognition system-based vision computing for computed tomography COVID images

Computed tomography (CT) image-based medical recognition is extensively used for COVID recognition as it improves recognition and scanning rate. A method for intelligent compression and recognition system-based vision computing for CT COVID (ICRS-VC-COVID) was developed. The proposed system first preprocesses lung CT COVID images. Segmentation is then used to split the image into two regions: nonregion of interest (NROI) with fractal lossy compression and region of interest with context tree weighting lossless. Subsequently, a fast discrete curvelet transform (FDCT) is applied. Finally, vector quantization is implemented through the encoder, channel, and decoder. Two experiments were conducted to test the proposed ICRS-VC-COVID. The first evaluated the segmentation compression, FDCT, wavelet transform, and discrete curvelet transform (DCT). The second evaluated the FDCT, wavelet transform, and DCT with segmentation. It demonstrates a significant improvement in performance parameters, such as mean square error, peak signal-to-noise ratio, and compression ratio. At similar computational complexity, the proposed ICRS-VC-COVID is superior to some existing techniques. Moreover, at the same bit rate, it significantly improves the quality of the image. Thus, the proposed method can enable lung CT COVID images to be applied for disease recognition with low computational power and space. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. [DOI: 10.1117/1.JEI.32.2.021404] © 2023 SPIE. All rights reserved.

VQ-ViCNet: Strengthen Unique Features Comparison Autoencoder with Embedding Space for Covid-19 Image Classification

In this paper, we propose a new novel coronavirus pneumonia image classification model based on the combination of Transformer and convolutional network(VQ-ViCNet), and present a vector quantization feature enhancement module for the inconspicuous characteristics of lung medical image data. This model extracts the local latent layer features of the image through the convolutional network, and learns the deep global features of the image data through the Transformer's multi-head self attention algorithm. After the calculation of convolution and attention, the features learned by the Transformer Encoder are enhanced by the vector quantization feature enhancement module and able to better complete the final downstream tasks. This model performs better than convolutional architectures, pure attention architectures and generative models on all 6 public datasets. © 2022 IEEE.

VQC-COVID-NET: Vector Quantization Contrastive Learning for Covid-19 Image Base Classification

Today, the COVID-19 epidemic has become extremely widespread. The first step in combating COVID-19 is identifying cases of infection. Real-time reverse transcriptase polymerase chain reaction is the most common method for identifying COVID (RT-PCR). This method, however, has been compromised by a time-consuming, laborious, and complex manual process. In addition to the RT-PCR test, screening computed tomography scan (CT) or X-ray images may be used to identify positive COVID-19 results, which could aid in the detection of COVID-19. Because of the continuing increase in new infections, the development of automated techniques for COVID-19 detection utilizing CT images is in high demand. This will aid in clinical diagnosis and alleviate the arduous task of image interpretation. Aggregating instances from various medical systems is highly advantageous for enlarging datasets for the development of machine learning techniques and the acquisition of robust, generalizable models. This study proposes a novel method for addressing distinct feature normalization in latent space due to cross-site domain shift in order to accurately execute COVID-19 identification using heterogeneous datasets with distribution disagreement. We propose using vector quantization to enhance the domain invariance of semantic embeddings in order to enhance classification performance on each dataset. We use two large, publicly accessible COVID-19 diagnostic CT scan datasets to develop and validate our proposed model. The experimental results demonstrate that our proposed method routinely outperforms state-of-the-art techniques on testing datasets. Public access to the implementation of our proposed method is available at https://github.com/khaclinh/VQC-COVID-NET. © 2022 IEEE.