Classifying Chest X-rays for COVID-19 using Deep Learning

A developing virus called COVID-19 infects the lungs and upper layer respiratory system. Medical imaging and PCR assays can be used to identify COVID-19. Medical images are used to identify COVID-19 diseases in the proposed classification model, which works well. A crucial step in the battle against this fatal illness may turn out to be an efficient screening and diagnostic phase in treating infected sufferers. Chest X-ray (CXR) scans could be used to do this. The utilization of chest X-ray imaging for early detection may prove to be a crucial strategy in the fight against COVID-19. Many computer- aided diagnostic (CAD) methods have been developed to help radiologists and provide them with more information for the same. In a training network with many classes, tertiary classification starts to become more accurate as the number of classes increases. © 2023 IEEE.

COVID-19 and Pneumonia Detection using X-ray Image-Based Deep Learning Methods

Globally, pneumonia is the leading cause of death for young people and children. An X-ray of the chest is usually used to diagnose pneumonia by a trained specialist. However, the process is tedious and can result in disagreements among radiologists. It is possible to improve diagnostic accuracy through the use of computer-aided diagnostic systems. In this work, the ResNet model was selected to work as the covid-19 and pneumonia detector based on X-ray image. Several experiments are conducted on to achieve an optimal results. © 2022 SPIE.

Learning from Small Datasets: An Efficient Deep Learning Model for Covid-19 Detection from Chest X-ray Using Dataset Distillation Technique

Ever since the spread of the Coronavirus pandemic popularly known as Covid-19, researchers have dwelled in finding ways to curtail the spread of this disease. The disease has no known treatment but the best way of reducing its spread is by conducting tests, to identify people with positive cases, and isolate them from the general public. Despite the efforts being made by medical practitioners and media houses to provide public awareness, the general public is still shunning away from the covid-19 tests, because of the sentiments rumored about the disease and the complications of the testing process. In some countries, even the cost of the tests is beyond the reach of common citizens or simply not affordable. Researchers proposed cost-effective deep learning models of detecting covid-19 from the chest x-ray images, to serve as a diagnostics aid or an improvised tool in places where the testing materials are not affordable or available. However, the models are very cumbersome, making them expensive to train, the model also suffers from a long inference time. As the matter of diagnosis is critical, it is necessary to provide a new faster models with shorter inference time. Therefore, this paper proposed a novel covid-19 diagnosis using dataset distillation. The model used only 70 instances out of 3,616 available instances in the X-ray dataset, making the model resource inexpensive, and faster to train. The performance of the proposed model achieved 95% accuracy when tested, the model also outperformed the convolutional neural network (CNN) model trained with a full dataset in terms of accuracy. © 2022 IEEE.

Chest X-ray analysis empowered with deep learning: A systematic review.

Chest radiographs are widely used in the medical domain and at present, chest X-radiation particularly plays an important role in the diagnosis of medical conditions such as pneumonia and COVID-19 disease. The recent developments of deep learning techniques led to a promising performance in medical image classification and prediction tasks. With the availability of chest X-ray datasets and emerging trends in data engineering techniques, there is a growth in recent related publications. Recently, there have been only a few survey papers that addressed chest X-ray classification using deep learning techniques. However, they lack the analysis of the trends of recent studies. This systematic review paper explores and provides a comprehensive analysis of the related studies that have used deep learning techniques to analyze chest X-ray images. We present the state-of-the-art deep learning based pneumonia and COVID-19 detection solutions, trends in recent studies, publicly available datasets, guidance to follow a deep learning process, challenges and potential future research directions in this domain. The discoveries and the conclusions of the reviewed work have been organized in a way that researchers and developers working in the same domain can use this work to support them in taking decisions on their research.

Deep Framework for Predicting COVID-19 and Related Lung diseases using CXR Images

Along with a health crisis, COVID-19 has also led the world towards an economical barrier. So far the virus has effected approximately 400 Millions causing 5 Million deaths and is expanding everyday. There is an urge to stop the exponential growth of the contagious disease, only possible through an early diagnosis of the disease. Currently, several testing techniques are being used to diagnose COVID-19, among them Polymerase Chain Reaction (PCR) is a gold standard globally. However, due to it's processing time, cost and less sensitivity towards COVID-19, physicians suggest to correlate the results with radiological tests preferably Chest X-Ray (CXR) imaging since it consumes less time and is more sensitive towards COVID-19. To overcome the pandemic many research groups have been working on the solution. Several Computer Aided Diagnostic (CAD) systems have been proposed by the researchers however, they lack robustness and stability towards blind datasets. Moreover, majority of the CAD systems provide binary classification between healthy and COVID-19, various lung abnormalities resembles COVID-19 in terms of their structural appearance and can be falsely classified as COVID-19. In this paper, we have proposed a deep model using EfficinetNet-B0 as a baseline model. Our proposed model has been trained on the largest available CXR dataset of COVID-19 comprising CXR images of normal, Viral Pneumonia, Lung Opacity and COVID-19 effected lungs and yielded an accuracy of 99.46%. Proposed model has been blind tested on four publicly available datasets achieving highest accuracy of 99.96%. Furthermore, the model is transfer learned and fine tuned on another publicly available CXR dataset and evaluated to be 85.26% accurate for 20 epochs. © 2022 IEEE.

Early Classification COVID-19 Based on Particle Swarm Optimization Algorithm Using CT-Images

Diagnosis is a critical preventative step in Coronavirus researches. Because of the fast spread of this virus, it is necessary to present a computer-aided diagnostic (CAD) system which is very faster for radiologists. Feature Selection (FS) is a significant technique to obtain an accurate CAD system. This paper presented an effective FS model which based on wrapper approach as evaluator and Particle Swarm Optimization (PSO) as search method for classifying cases of COVID-19 using Computed Tomography (CT). This model was used PSO algorithm to identify the significant features subset within overall features set. Support Vector Machine (SVM), K-nearest neighbor (KNN) classifiers were used as evaluators with 10-fold cross validation and reached accuracy of 99.6% for SVM and 94.27% for KNN respectively. The results were shown that proposed PSO-FS model is an intelligent and outperforms other two traditional FS search methods, Genetic Algorithm (GA) and Greedy Stepwise (GS). © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.