Fast Unsupervised Residual Attention GAN for COVID-19 Detection

Recently, deep unsupervised learning methods based on Generative Adversarial Networks (GANs) have shown great potential for detecting anomalies. These last can appear both in global and local areas of an image. Consequently, ignoring these local information may lead to unreliable detection of anomalies. In this paper, we propose a residual GAN-based unsupervised learning approach capable of detecting anomalies at both image and pixel levels. Our method is applied for COVID-19 detection, it is based on the BigGAN model to ensure high-quality generated images, also it adds attention modules to capture spatial and channel-wise features to enhance salient regions and extract more detailed features. The proposed model is composed of three components: a generator, a discriminator, and an encoder. The encoder enables a fast mapping from images to the latent space, which facilitates the evaluation of unseen images. We evaluate the proposed method with by real-world benchmarking datasets and a public COVID-19 dataset and we illustrate the performance improvement at image and pixel levels.

VVC intra prediction decoder: Feature improvement and performance analysis

Nowadays, streaming applications have been in great demand, especially due to covid-19 (teleworking, online teaching, virtual reality, etc.). In addition, artificial intelligence has become widely used especially in video processing domains, so a video with high quality improves the accuracy rate of this application. To meet these needs, the Versatile Video Coding standard (VVC) has appeared to give a high compression efficiency compared to high-efficiency video coding. This norm consists of a high complexity algorithm that offers an improvement in processing time and decreases the bit rate by 50 % thanks to several new compression techniques. In this context, we propose the implementation of an intra prediction decoding chain of this standard on a system on chip. In this work, we highlight the VVC feature enhancements, we present the suitable method for VVC intra-prediction decoder implementation on the PYNQ-Z2, and we provide profiling in terms of decoding time and power consumption. As a future work, this study is helpful to distinguish the block that will be a candidate for a Hardware acceleration. © 2022 IEEE.

A Deep Learning-Based Diagnosis System for COVID-19 Detection and Pneumonia Screening Using CT Imaging

Background: Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a global threat impacting the lives of millions of people worldwide. Automated detection of lung infections from Computed Tomography scans represents an excellent alternative;however, segmenting infected regions from CT slices encounters many challenges. Objective: Developing a diagnosis system based on deep learning techniques to detect and quantify COVID-19 infection and pneumonia screening using CT imaging. Method: Contrast Limited Adaptive Histogram Equalization pre-processing method was used to remove the noise and intensity in homogeneity. Black slices were also removed to crop only the region of interest containing the lungs. A U-net architecture, based on CNN encoder and CNN decoder approaches, is then introduced for a fast and precise image segmentation to obtain the lung and infection segmentation models. For better estimation of skill on unseen data, a fourfold cross-validation as a resampling procedure has been used. A three-layered CNN architecture, with additional fully connected layers followed by a Softmax layer, was used for classification. Lung and infection volumes have been reconstructed to allow volume ratio computing and obtain infection rate. Results: Starting with the 20 CT scan cases, data has been divided into 70% for the training dataset and 30% for the validation dataset. Experimental results demonstrated that the proposed system achieves a dice score of 0.98 and 0.91 for the lung and infection segmentation tasks, respectively, and an accuracy of 0.98 for the classification task. Conclusions: The proposed workflow aimed at obtaining good performances for the different system’s components, and at the same time, dealing with reduced datasets used for training. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

A Novel Deep Learning Model for COVID-19 Detection from Combined Heterogeneous X-ray and CT Chest Images

COVID-19 originally started in Wuhan city in China. The disease rapidly became a worldwide pandemic, causing a respiratory illness with symptoms such as coughing, fever, and in more severe cases difficulty in breathing. With the current testing processes, it is very difficult and sometimes impossible to manage and provide the necessary treatment to suspected patients since the number of the infected is rapidly increasing. Hence, the availability of an artificial intelligent driven system can be an assistive tool to provide accurate diagnosis using radiology imaging techniques. In this paper, we put forward a new deep learning architecture, which integrates the Nested Residual Connections (NRCs) in a DarkCovidNet model, called DarkCovidNet-NRC, in order to classify chest images and to detect COVID-19 cases. The proposed architecture is validated with the K-fold cross-validation technique on X-ray and CT chest datasets separately and then combined. The experimental results reveal that the suggested model performs very well in the medical classification task and it competes with the state of the art in multiple performance metrics by respectively achieving an accuracy and precision of 0.9609 and 0.978 on the combined dataset. © 2021, Springer Nature Switzerland AG.