TEDGE-Caching: Transformer-based Edge Caching Towards 6G Networks

As a consequence of the COVID-19 pandemic, the demand for telecommunication for remote learning/working and telemedicine has significantly increased. Mobile Edge Caching (MEC) in the 6G networks has been evolved as an efficient solution to meet the phenomenal growth of the global mobile data traffic by bringing multimedia content closer to the users. Although massive connectivity enabled by MEC networks will significantly increase the quality of communications, there are several key challenges ahead. The limited storage of edge nodes, the large size of multimedia content, and the time-variant users' preferences make it critical to efficiently and dynamically predict the popularity of content to store the most upcoming requested ones before being requested. Recent advancements in Deep Neural Networks (DNNs) have drawn much research attention to predict the content popularity in proactive caching schemes. Existing DNN models in this context, however, suffer from long-term dependencies, computational complexity, and unsuitability for parallel computing. To tackle these challenges, we propose an edge caching framework incorporated with the attention-based Vision Transformer (ViT) neural network, referred to as the Transformer-based Edge (TEDGE) caching, which to the best of our knowledge, is being studied for the first time. Moreover, the TEDGE caching framework requires no data pre-processing and additional contextual information. Simulation results corroborate the effectiveness of the proposed TEDGE caching framework in comparison to its counterparts. © 2022 IEEE.

CT-CAPS: FEATURE EXTRACTION-BASED AUTOMATED FRAMEWORK FOR COVID-19 DISEASE IDENTIFICATION FROM CHEST CT SCANS USING CAPSULE NETWORKS

The global outbreak of the novel corona virus (COVID-19) disease has drastically impacted the world and led to one of the most challenging crisis across the globe since World War II. The early diagnosis and isolation of COVID-19 positive cases are considered as crucial steps towards preventing the spread of the disease and flattening the epidemic curve. Chest Computed Tomography (CT) scan is a highly sensitive, rapid, and accurate diagnostic technique that can complement Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. Recently, deep learning-based models, mostly based on Convolutional Neural Networks (CNN), have shown promising diagnostic results. CNNs, however, are incapable of capturing spatial relations between image instances and require large datasets. Capsule Networks, on the other hand, can capture spatial relations, require smaller datasets, and have considerably fewer parameters. In this paper, a Capsule network framework, referred to as the "CT-CAPS", is presented to automatically extract distinctive features of chest CT scans. These features, which are extracted from the layer before the final capsule layer, are then leveraged to differentiate COVID-19 from Non-COVID cases. The experiments on our in-house dataset of 307 patients show the state-of-the-art performance with the accuracy of 90.8%, sensitivity of 94.5%, and specificity of 86.0%.

Wso-caps: Diagnosis of lung infection from low and ultra-lowdose CT scans using capsule networks and windowsetting optimization

The automatic diagnosis of lung infections using chest computed tomography (CT) scans has been recently obtained remarkable significance, particularly during the COVID-19 pandemic that the early diagnosis of the disease is of utmost importance. In addition, infection diagnosis is the main building block of most automated diagnostic/prognostic frameworks. Recently, due to the devastating effects of the radiation on the body caused by the CT scan, there has been a surge in acquiring low and ultra-low-dose CT scans instead of the standard scans. Such CT scans, however, suffer from a high noise level which makes them difficult and time-consuming to interpret even by expert radiologists. In addition, some abnormalities are only visible using specific window settings on the radiologists' monitor. Currently, manual adjustment of the windowing settings is the common approach to analyze such low-quality images. In this paper, we propose an automated framework based on the Capsule Networks, referred to as the 'WSO-CAPS', to detect slices demonstrating infection using low and ultra-low-dose chest CT scans. The WSOCAPS framework is equipped with a Window Setting Optimization (WSO) mechanism to automatically identify the best window setting parameters to resemble the radiologists' efforts. The experimental results on our in-house dataset show that the WSO-CAPS enhances the capability of the Capsule Network and its counterparts to identify slices demonstrating infection. The WSO-CAPS achieves the accuracy of 92.0%, sensitivity of 90.3%, and specificity of 93.3%. We believe that the proposed WSO-CAPS has a high potential to be further utilized in future frameworks that are working with CT scans, particularly the ones which utilize an infection diagnosis step in their pipeline. © 2021 IEEE.

An ensemble learning framework for multi-class Covid-19 lesion segmentation from chest Ct images

The novel Coronavirus disease (COVID-19) has been the most critical global challenge over the past months. Lung involvement quantification and distinguishing the types of infections from chest CT scans can assist in accurate severity assessment of COVID-19 pneumonia, efficient use of limited medical resources, and saving more lives. Nevertheless, visual assessment of chest CT images and evaluating the disease severity by radiologists are expensive and prone to error. This paper proposes an automated deep learning (DL)-based framework for multi-class segmentation of COVID lesions from chest CT images that takes the CT images as the input and generates a mask indicating the infection regions. The infection regions are segmented under two classes of data, GGOs and consolidations, which are the most common CT patterns of COVID-19 pneumonia. The proposed end-to-end framework contains four encoder-decoder-based segmentation networks that exploit the top-performing pretrained CNNs as the encoder paths and are developed and trained separately. The results then are aggregated using a pixel-level Soft Majority Voting to obtain the final class membership probabilities for each pixel of the image. The proposed framework is evaluated using an open-access CT segmentation dataset. The experimental results indicate that our method successfully performs multi-class segmenting of COVID-19 lung infection regions and outperforms previous works. © 2021 IEEE.

COVID-CT-MD, COVID-19 computed tomography scan dataset applicable in machine learning and deep learning

Novel Coronavirus (COVID-19) has drastically overwhelmed more than 200 countries affecting millions and claiming almost 2 million lives, since its emergence in late 2019. This highly contagious disease can easily spread, and if not controlled in a timely fashion, can rapidly incapacitate healthcare systems. The current standard diagnosis method, the Reverse Transcription Polymerase Chain Reaction (RT- PCR), is time consuming, and subject to low sensitivity. Chest Radiograph (CXR), the first imaging modality to be used, is readily available and gives immediate results. However, it has notoriously lower sensitivity than Computed Tomography (CT), which can be used efficiently to complement other diagnostic methods. This paper introduces a new COVID-19 CT scan dataset, referred to as COVID-CT-MD, consisting of not only COVID-19 cases, but also healthy and participants infected by Community Acquired Pneumonia (CAP). COVID-CT-MD dataset, which is accompanied with lobe-level, slice-level and patient-level labels, has the potential to facilitate the COVID-19 research, in particular COVID-CT-MD can assist in development of advanced Machine Learning (ML) and Deep Neural Network (DNN) based solutions.