Investigation and Implementation of Convolutional Neural Networks with Transfer Learning for Detection of Covid-19

This paper aims to present a robust model which uses Artificial Intelligence in the rapid and accurate detection of Covid-19. The Proposed Project uses Deep Learning techniques for the detection of Covid-19 as well as Pneumonia with the help of Digitalized Chest X-rays. By Applying Pre-Trained Networks which are also known as Convolutional Neural Networks, this can be made possible. Also, with the help of Transfer Learning Techniques the Neural Networks can be trained and validated faster and better thus providing a significantly higher chance in the correct and accurate detection. The CNNs taken into consideration are VGG-19 and ResNet-50 with the former providing an accuracy of over 95% and the latter with an accuracy of 92% proving that such a high accuracy computer-aided diagnostic tool can be used at a time like this. Also, further improvements in the future with advancements in technology can provide even astonishing results.

A Hybrid Automated Intelligent COVID-19 Classification System Based on Neutrosophic Logic and Machine Learning Techniques Using Chest X-Ray Images

To facilitate timely treatment and management of COVID-ap patients, efficient and quick identification of COVID-19 patients is of immense importance during the COVID-19 crisis. Technological developments in machine learning (ML) methods, edge computing, computer-aided medical diagnostic been utilized for COVID-19 Classification. This is mainly because of their ability to deal with Big data and their inherent robustness and ability to provide distinct output characteristics attributed to the underlying application. The contrary transcriptionpolymerase chain reaction is currently the clinical typical for COVID-19 diagnosis. Besides being expensive, it has low sensitivity and requires expert medical personnel. Compared with RT-PCR, chest X-rays are easily accessible with highly available annotated datasets and can be utilized as an ascendant alternative in COVID-19 diagnosis. Using X-rays, ML methods can be employed to identify COVID-19 patients by quantitively examining chest X-rays effectively. Therefore, we introduce an alternative, robust, and intelligent diagnostic tool for automatically detecting COVID-19 utilizing available resources from digital chest X-rays. Our technique is a hybrid framework that is based on the fusion of two techniques, Neutrosophic techniques (NTs) and ML. Classification features are extracted from X-ray images using morphological features (MFs) and principal component analysis (PCA). The ML networks were trained to classify the chest X-rays into two classes: positive (+ve) COVID-19 patients or normal subjects (or -ve). The experimental results are performed based on a sample from a collected comprehensive image dataset from several hospitals worldwide. The classification accuracy, precision, sensitivity, specificity and F1-score for the proposed scheme was 98.46%, 98.19%, 98.18%, 98.67%, and 98.17%. The experimental results also documented the high accuracy of the proposed pipeline compared to other literature techniques.

Deep Learning for Reliable Classification of COVID-19, MERS, and SARS from Chest X-ray Images.

Novel coronavirus disease (COVID-19) is an extremely contagious and quickly spreading coronavirus infestation. Severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), which outbreak in 2002 and 2011, and the current COVID-19 pandemic are all from the same family of coronavirus. This work aims to classify COVID-19, SARS, and MERS chest X-ray (CXR) images using deep convolutional neural networks (CNNs). To the best of our knowledge, this classification scheme has never been investigated in the literature. A unique database was created, so-called QU-COVID-family, consisting of 423 COVID-19, 144 MERS, and 134 SARS CXR images. Besides, a robust COVID-19 recognition system was proposed to identify lung regions using a CNN segmentation model (U-Net), and then classify the segmented lung images as COVID-19, MERS, or SARS using a pre-trained CNN classifier. Furthermore, the Score-CAM visualization method was utilized to visualize classification output and understand the reasoning behind the decision of deep CNNs. Several deep learning classifiers were trained and tested; four outperforming algorithms were reported: SqueezeNet, ResNet18, InceptionV3, and DenseNet201. Original and preprocessed images were used individually and all together as the input(s) to the networks. Two recognition schemes were considered: plain CXR classification and segmented CXR classification. For plain CXRs, it was observed that InceptionV3 outperforms other networks with a 3-channel scheme and achieves sensitivities of 99.5%, 93.1%, and 97% for classifying COVID-19, MERS, and SARS images, respectively. In contrast, for segmented CXRs, InceptionV3 outperformed using the original CXR dataset and achieved sensitivities of 96.94%, 79.68%, and 90.26% for classifying COVID-19, MERS, and SARS images, respectively. The classification performance degrades with segmented CXRs compared to plain CXRs. However, the results are more reliable as the network learns from the main region of interest, avoiding irrelevant non-lung areas (heart, bones, or text), which was confirmed by the Score-CAM visualization. All networks showed high COVID-19 detection sensitivity (> 96%) with the segmented lung images. This indicates the unique radiographic signature of COVID-19 cases in the eyes of AI, which is often a challenging task for medical doctors.

Role of Artificial Intelligence in COVID-19 Detection.

The global pandemic of coronavirus disease (COVID-19) has caused millions of deaths and affected the livelihood of many more people. Early and rapid detection of COVID-19 is a challenging task for the medical community, but it is also crucial in stopping the spread of the SARS-CoV-2 virus. Prior substantiation of artificial intelligence (AI) in various fields of science has encouraged researchers to further address this problem. Various medical imaging modalities including X-ray, computed tomography (CT) and ultrasound (US) using AI techniques have greatly helped to curb the COVID-19 outbreak by assisting with early diagnosis. We carried out a systematic review on state-of-the-art AI techniques applied with X-ray, CT, and US images to detect COVID-19. In this paper, we discuss approaches used by various authors and the significance of these research efforts, the potential challenges, and future trends related to the implementation of an AI system for disease detection during the COVID-19 pandemic.