Deep Learning System for COVID-19 Diagnosis Aid Using X-ray Pulmonary Images

The spread of the SARS-CoV-2 virus has made the COVID-19 disease a worldwide epidemic. The most common tests to identify COVID-19 are invasive, time consuming and limited in resources. Imaging is a non-invasive technique to identify if individuals have symptoms of disease in their lungs. However, the diagnosis by this method needs to be made by a specialist doctor, which limits the mass diagnosis of the population. Image processing tools to support diagnosis reduce the load by ruling out negative cases. Advanced artificial intelligence techniques such as Deep Learning have shown high effectiveness in identifying patterns such as those that can be found in diseased tissue. This study analyzes the effectiveness of a VGG16-based Deep Learning model for the identification of pneumonia and COVID-19 using torso radiographs. Results show a high sensitivity in the identification of COVID-19, around 100%, and with a high degree of specificity, which indicates that it can be used as a screening test. AUCs on ROC curves are greater than 0.9 for all classes considered

Performance Evaluation of the NASNet Convolutional Network in the Automatic Identification of COVID-19

This paper evaluates the performance of the Neural Architecture Search Network (NASNet) in the automatic detection of COVID-19 (Coronavirus Disease 2019) from chest x-ray images. COVID-19 is a disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) that produces in patients fever, cough, shortness of breath, muscle pain, sputum production, diarrhea, and even sore throat. The virus spreads through the air, and to date, is expanding as a global pandemic. There is no vaccine, and it is fatal to approximately 2-7% of the infected population. Among the clinical and paraclinical characteristics of infected patients, nodules have been identified in images of chest x-rays that can be visually identified, producing a simple, rapid, and generally available method of identification. However, the rapid spread of the disease means that there is a lack of specialized medical personnel capable of identifying it, which is why automated schemes are being developed. We propose the tuning of a NASNet-type convolutional model to automatically determine the initial state of a patient in the triage process or intervention protocol of health care centers. The neural network is trained with public images of cases positively identified as patients infected with the virus and patients in normal conditions without infection. Performance evaluation is also done with real images unknown to the neuronal model. As for performance metrics, we use the function of loss of cross-entropy (categorical cross-entropy), the accuracy (or success rate), and the MSE (Mean Squared Error). The tuned model was able to correctly classify the test images with an accuracy of 97%

Discovering the Symptom Patterns of COVID-19 from Recovered and Deceased Patients Using Apriori Association Rule Mining

The COVID-19 pandemic has a devastating impact globally, claiming millions of lives and causing significant social and economic disruptions. In order to optimize decision-making and allocate limited resources, it is essential to identify COVID-19 symptoms and determine the severity of each case. Machine learning algorithms offer a potent tool in the medical field, particularly in mining clinical datasets for useful information and guiding scientific decisions. Association rule mining is a machine learning technique for extracting hidden patterns from data. This paper presents an application of association rule mining based Apriori algorithm to discover symptom patterns from COVID-19 patients. The study, using 2875 records of patient, identified the most common symptoms as apnea (72%), cough (64%), fever (59%), weakness (18%), myalgia (14.5%), and sore throat (12%). The proposed method provides clinicians with valuable insight into disease that can assist them in managing and treating it effectively

Study of Different Deep Learning Approach with Explainable AI for Screening Patients with COVID-19 Symptoms: Using CT Scan and Chest X-ray Image Dataset

The outbreak of COVID-19 disease caused more than 100,000 deaths so far in the USA alone. It is necessary to conduct an initial screening of patients with the symptoms of COVID-19 disease to control the spread of the disease. However, it is becoming laborious to conduct the tests with the available testing kits due to the growing number of patients. Some studies proposed CT scan or chest X-ray images as an alternative solution. Therefore, it is essential to use every available resource, instead of either a CT scan or chest X-ray to conduct a large number of tests simultaneously. As a result, this study aims to develop a deep learning-based model that can detect COVID-19 patients with better accuracy both on CT scan and chest X-ray image dataset. In this work, eight different deep learning approaches such as VGG16, InceptionResNetV2, ResNet50, DenseNet201, VGG19, MobilenetV2, NasNetMobile, and ResNet15V2 have been tested on two dataset-one dataset includes 400 CT scan images, and another dataset includes 400 chest X-ray images studied. Besides, Local Interpretable Model-agnostic Explanations (LIME) is used to explain the model's interpretability. Using LIME, test results demonstrate that it is conceivable to interpret top features that should have worked to build a trust AI framework to distinguish between patients with COVID-19 symptoms with other patients.Comment: This is a work in progress, it should not be relied upon without context to guide clinical practice or health-related behavior and should not be reported in news media as established information without consulting multiple experts in the fiel

Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network

Chest X-ray is the first imaging technique that plays an important role in the diagnosis of COVID-19 disease. Due to the high availability of large-scale annotated image datasets, great success has been achieved using convolutional neural networks (CNN s) for image recognition and classification. However, due to the limited availability of annotated medical images, the classification of medical images remains the biggest challenge in medical diagnosis. Thanks to transfer learning, an effective mechanism that can provide a promising solution by transferring knowledge from generic object recognition tasks to domain-specific tasks. In this paper, we validate and a deep CNN, called Decompose, Transfer, and Compose (DeTraC), for the classification of COVID-19 chest X-ray images. DeTraC can deal with any irregularities in the image dataset by investigating its class boundaries using a class decomposition mechanism. The experimental results showed the capability of DeTraC in the detection of COVID-19 cases from a comprehensive image dataset collected from several hospitals around the world. High accuracy of 93.1% (with a sensitivity of 100%) was achieved by DeTraC in the detection of COVID-19 X-ray images from normal, and severe acute respiratory syndrome cases