COVID-19 X-RAY IMAGE DETECTION ALGORITHM BASED ON DEEP LEARNING

In order to enhance the ability to diagnose and distinguish COVID-19 from ordinary pneumonia, and to assist medical staff in chest X-ray detection of pneumonia patients, this paper proposes a COVID-19 X-ray image detection algorithm based on deep learning network. First of all, a model of deep learning network is set up based on VGG - 16, and then, the network structure and parameter optimization is adjusted, which makes the network model can be applied to COVID - 19 x ray imaging detection task. In the end, through adjusting the image size of the original data set, the input data meets the requirements of the deep learning network. Experimental results show that the proposed algorithm can effectively learn the characteristics of the COVID-19 X-ray image data set and accurately detect three states of COVID-19, common viral pneumonia and non-pneumonia, with a very high detection accuracy of 95.8%. © 2023 SPIE.

Study of X Ray Detection Using CNN in Machine Learning

The coronavirus spread that started in Wuhan, China and spread across the world, affecting the best of the healthcare systems from the Lombardy region of Italy to India, the US, and the UK, required accurate diagnosis. A rapid assessment to ascertain whether or not a patient has COVID-19 is required by frontline clinicians. In this paper, we propose to deduce the presence of COVID-19 using X-ray images of the lungs through feature extraction. A convolution network model is built for binary classification of images into corona positive and negative using the deep learning framework on Python, Keras. Various studies using different classifiers such as CART, XGB-L and XGB Tree were studied, which used machine learning for detection of COVID-19 and yielded a very accurate diagnosis. In this particular CNN model, Google Colab is used to execute the algorithm. The dataset is trained and the validation accuracy obtained is more than 96%. This is a very cost-effective way of using machine learning for the classification of infected and non-infected cases since working on Google Colab doesn't require enormous computational resources. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Feature Adaptation Predictive Coding for Quantized Block Compressive Sensing of COVID-19 X-Ray Images

With the development of remote X-ray detection for Corona Virus Disease 2019 (COVID-19), the quantized block compressive sensing technology plays an important role when remotely acquiring the chest X-ray images of COVID-19 infected people and significantly promoting the portable telemedicine imaging applications. In order to improve the encoding performance of quantized block compressive sensing, a feature adaptation predictive coding (FAPC) method is proposed for the remote transmission of COVID-19 X-ray images. The proposed FAPC method can adaptively calculate the block-wise prediction coefficients according to the main features of COVID-19 X-ray images, and thus provide the optimal prediction candidate from the feature-guided candidate set. The proposed method can implement the high-efficiency encoding of X-ray images, and then swiftly transmit the telemedicine-oriented chest images. The experimental results show that compared with the state-of-the-art predictive coding methods, both rate-distortion and complexity performance of our FAPC method have enough competitive advantages. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Detection of Covid-19 in CXR: A Low Sample Size Deep Convolutional Neural Network Training Data Approach

Infectious illness Covid-19 is highly contagious and has claimed the lives of numerous individuals. To assist prevent the virus's transmission, it's critical to identify and isolate those who have been infected with the infection. The purpose of the study is to aid in the detection of Covid-19 alongside with RT-PCR test by utilizing a deep learning algorithm, specifically YOLOv3 as the technique to be used for it uses CNN, which then implements deep learning technique. The study has a promising detection to detect if the person's CXR has Covid-19, normal or viral pneumonia, obtaining an mAP value of 95.27% from model 14, which is the highest among the 12 models created. © 2022 IEEE.

One-Class Classifier for Chest X-ray Anomaly Detection via Contrastive Patch-based Percentile

Given its low dose and compactness, chest radiography has been widely used as the first-line test to determine the presence of lung anomalies. Nevertheless, a high-performance diagnosis for initial screening to detect shadows in lungs due to general lung diseases is not available. During initial screening, chest radiography can be used to distinguish any diseased lung shadowing caused by lung diseases. Thus, chest radiography can contribute to the early diagnosis and prevention of novel lung infectious diseases if training for a specific disease is not required. Accordingly, we propose a deep-learning-based diagnostic system called contrast-shifted instances via patch-based percentile (CSIP) to automatically detect diseased lung shadowing via training only on chest X-ray data from healthy subjects. CSIP is the first application of a patch-based percentile approach to state-of-the-art one-class classifiers (OCCs). This application improves the sensitivity of the network to recognize shadowing density differences in each local area of the lung, thereby considerably improving the diagnostic performance of average area under the curve (AUC) by more than 20% and achieving a sufficiently high diagnostic performance (average AUC of 0.96 for various lung diseases), compared to the existing OCC case without applying our patch-based approach (average AUC of 0.74). Therefore, CSIP may contribute to the early detection of anomalies caused by novel infectious diseases such as variants of the coronavirus disease, for whom training data are scarce. The code is available at https://github.com/kskim-phd/CSIP. Author