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General General

Accurate computation: COVID-19 rRT-PCR positive test dataset using stages classification through textual big data mining with machine learning.

In The Journal of supercomputing

In every field of life, advanced technology has become a rapid outcome, particularly in the medical field. The recent epidemic of the coronavirus disease 2019 (COVID-19) has promptly become outbreaks to identify early action from suspected cases at the primary stage over the risk prediction. It is overbearing to progress a control system that will locate the coronavirus. At present, the confirmation of COVID-19 infection by the ideal standard test of reverse transcription-polymerase chain reaction (rRT-PCR) by the extension of RNA viral, although it presents identified from deficiencies of long reversal time to generate results in 2-4 h of corona with a necessity of certified laboratories. In this proposed system, a machine learning (ML) algorithm is used to classify the textual clinical report into four classes by using the textual data mining method. The algorithm of the ensemble ML classifier has performed feature extraction using the advanced techniques of term frequency-inverse document frequency (TF/IDF) which is an effective information retrieval technique from the corona dataset. Humans get infected by coronaviruses in three ways: first, mild respiratory disease which is globally pandemic, and human coronaviruses are caused by HCoV-NL63, HCoV-OC43, HCoV-HKU1, and HCoV-229E; second, the zoonotic Middle East respiratory syndrome coronavirus (MERS-CoV); and finally, higher case casualty rate defined as severe acute respiratory syndrome coronavirus (SARS-CoV). By using the machine learning techniques, the three-way COVID-19 stages are classified by the extraction of the feature using the data retrieval process. The TF/IDF is used to measure and evaluate statistically the text data mining of COVID-19 patient's record list for classification and prediction of the coronavirus. This study established the feasibility of techniques to analyze blood tests and machine learning as an alternative to rRT-PCR for detecting the category of COVID-19-positive patients.

Ramanathan Shalini, Ramasundaram Mohan

2021-Jan-04

COVID-19, Classification, Feature extraction, Machine learning, RT-PCR test, TF-IDF, Text data mining

General General

Pneumonia Classification Using Deep Learning from Chest X-ray Images During COVID-19.

In Cognitive computation

The outbreak of the novel corona virus disease (COVID-19) in December 2019 has led to global crisis around the world. The disease was declared pandemic by World Health Organization (WHO) on 11th of March 2020. Currently, the outbreak has affected more than 200 countries with more than 37 million confirmed cases and more than 1 million death tolls as of 10 October 2020. Reverse-transcription polymerase chain reaction (RT-PCR) is the standard method for detection of COVID-19 disease, but it has many challenges such as false positives, low sensitivity, expensive, and requires experts to conduct the test. As the number of cases continue to grow, there is a high need for developing a rapid screening method that is accurate, fast, and cheap. Chest X-ray (CXR) scan images can be considered as an alternative or a confirmatory approach as they are fast to obtain and easily accessible. Though the literature reports a number of approaches to classify CXR images and detect the COVID-19 infections, the majority of these approaches can only recognize two classes (e.g., COVID-19 vs. normal). However, there is a need for well-developed models that can classify a wider range of CXR images belonging to the COVID-19 class itself such as the bacterial pneumonia, the non-COVID-19 viral pneumonia, and the normal CXR scans. The current work proposes the use of a deep learning approach based on pretrained AlexNet model for the classification of COVID-19, non-COVID-19 viral pneumonia, bacterial pneumonia, and normal CXR scans obtained from different public databases. The model was trained to perform two-way classification (i.e., COVID-19 vs. normal, bacterial pneumonia vs. normal, non-COVID-19 viral pneumonia vs. normal, and COVID-19 vs. bacterial pneumonia), three-way classification (i.e., COVID-19 vs. bacterial pneumonia vs. normal), and four-way classification (i.e., COVID-19 vs. bacterial pneumonia vs. non-COVID-19 viral pneumonia vs. normal). For non-COVID-19 viral pneumonia and normal (healthy) CXR images, the proposed model achieved 94.43% accuracy, 98.19% sensitivity, and 95.78% specificity. For bacterial pneumonia and normal CXR images, the model achieved 91.43% accuracy, 91.94% sensitivity, and 100% specificity. For COVID-19 pneumonia and normal CXR images, the model achieved 99.16% accuracy, 97.44% sensitivity, and 100% specificity. For classification CXR images of COVID-19 pneumonia and non-COVID-19 viral pneumonia, the model achieved 99.62% accuracy, 90.63% sensitivity, and 99.89% specificity. For the three-way classification, the model achieved 94.00% accuracy, 91.30% sensitivity, and 84.78%. Finally, for the four-way classification, the model achieved an accuracy of 93.42%, sensitivity of 89.18%, and specificity of 98.92%.

Ibrahim Abdullahi Umar, Ozsoz Mehmet, Serte Sertan, Al-Turjman Fadi, Yakoi Polycarp Shizawaliyi

2021-Jan-04

AlexNet, Bacterial pneumonia, COVID-19, Chest X-rays images (CXR), Non-COVID-19 viral pneumonia

Pathology Pathology

Evaluating the Effects of An Artificial Intelligence System on Endoscopy Quality and Preliminarily Testing its Performance on Detecting Early Gastric Cancer: A Randomized Controlled Trial.

In Endoscopy ; h5-index 58.0

BACKGROUND AND STUDY AIMS : Qualified esophagogastroduodenoscopy (EGD) is a prerequisite for detecting upper gastrointestinal lesions especially early gastric cancer (EGC). Our previous report showed that artificial intelligence system could monitor blind spots during EGD. Here, we updated the system to a new one (named ENDOANGEL), verified its effectiveness on improving endoscopy quality and pre-tested its performance on detecting EGC in a multi-center randomized controlled trial.

PATIENTS AND METHODS : ENDOANGEL was developed using deep convolutional neural networks and deep reinforcement learning. Patients undergoing EGD examination in 5 hospitals were randomly assigned to ENDOANGEL-assisted (EA) group or normal control (NC) group. The primary outcome was the number of blind spots. The second outcome includes performance of ENDOANGEL on predicting EGC in clinical setting.

RESULTS : 1,050 patients were recruited and randomized. 498 and 504 patients in EA and NC groups were respectively analyzed. Compared with NC, the number of blind spots was less (5.382±4.315 vs. 9.821±4.978, p<0.001) and the inspection time was prolonged (5.400±3.821 min vs. 4.379±3.907 min, p<0.001) in EA group. In the 498 patients from EA group, 196 gastric lesions with pathological results were identified. ENDOANGEL correctly predicted all 3 EGC (1 mucosal carcinoma and 2 high-grade neoplasia) and 2 advanced gastric cancer, with a per-lesion accuracy of 84.69%, sensitivity of 100% and specificity of 84.29% for detecting GC.

CONCLUSIONS : The results of the multi-center study confirmed that ENDOANGEL is an effective and robust system to improve the quality of EGD and has the potential to detect EGC in real time.

Wu Lianlian, He Xinqi, Liu Mei, Xie Huaping, An Ping, Zhang Jun, Zhang Heng, Ai Yaowei, Tong Qiaoyun, Guo Mingwen, Huang Manling, Ge Cunjin, Yang Zhi, Yuan Jingping, Liu Jun, Zhou Wei, Jiang Xiaoda, Huang Xu, Mu Ganggang, Wan Xinyue, Li Yanxia, Wang Hongguang, Wang Yonggui, Zhang Hongfeng, Chen Di, Gong Dexin, Wang Jing, Huang Li, Li Jia, Yao Liwen, Zhu Yijie, Yu Honggang

2021-Jan-11

General General

Designing a hybrid reinforcement learning based algorithm with application in prediction of the COVID-19 pandemic in Quebec.

In Annals of operations research

World Health Organization (WHO) stated COVID-19 as a pandemic in March 2020. Since then, 26,795,847 cases have been reported worldwide, and 878,963 lost their lives due to the illness by September 3, 2020. Prediction of the COVID-19 pandemic will enable policymakers to optimize the use of healthcare system capacity and resource allocation to minimize the fatality rate. In this research, we design a novel hybrid reinforcement learning-based algorithm capable of solving complex optimization problems. We apply our algorithm to several well-known benchmarks and show that the proposed methodology provides quality solutions for most complex benchmarks. Besides, we show the dominance of the offered method over state-of-the-art methods through several measures. Moreover, to demonstrate the suggested method's efficiency in optimizing real-world problems, we implement our approach to the most recent data from Quebec, Canada, to predict the COVID-19 outbreak. Our algorithm, combined with the most recent mathematical model for COVID-19 pandemic prediction, accurately reflected the future trend of the pandemic with a mean square error of 6.29E-06. Furthermore, we generate several scenarios for deepening our insight into pandemic growth. We determine essential factors and deliver various managerial insights to help policymakers making decisions regarding future social measures.

Khalilpourazari Soheyl, Hashemi Doulabi Hossein

2021-Jan-03

COVID-19 pandemic, Machine learning, Reinforcement learning, SARS-Cov-2, SIDARTHE

General General

A Study of the Neutrosophic Set Significance on Deep Transfer Learning Models: an Experimental Case on a Limited COVID-19 Chest X-ray Dataset.

In Cognitive computation

Coronavirus, also known as COVID-19, has spread to several countries around the world. It was announced as a pandemic disease by The World Health Organization (WHO) in 2020 for its devastating impact on humans. With the advancements in computer science algorithms, the detection of this type of virus in the early stages is urgently needed for the fast recovery of patients. In this paper, a study of neutrosophic set significance on deep transfer learning models will be presented. The study will be conducted over a limited COVID-19 x-ray. The study relies on neutrosophic set and theory to convert the medical images from the grayscale spatial domain to the neutrosophic domain. The neutrosophic domain consists of three types of images, and they are the True (T) images, the Indeterminacy (I) images, and the Falsity (F) images. The dataset used in this research has been collected from different sources. The dataset is classified into four classes {COVID-19, normal, pneumonia bacterial, and pneumonia virus}. This study aims to review the effect of neutrosophic sets on deep transfer learning models. The selected deep learning models in this study are Alexnet, Googlenet, and Restnet18. Those models are selected as they have a small number of layers on their architectures. To test the performance of the conversion to the neutrosophic domain, more than 36 trials have been conducted and recorded. A combination of training and testing strategies by splitting the dataset into (90-10%, 80-20%, 70-30) is included in the experiments. Four domains of images are tested, and they are, the original domain, the True (T) domain, the Indeterminacy (I) domain, and the Falsity (F) domain. The four domains with the different training and testing strategies were tested using the selected deep transfer models. According to the experimental results, the Indeterminacy (I) neutrosophic domain achieves the highest accuracy possible with 87.1% in the testing accuracy and performance metrics such as Precision, Recall, and F1 Score. The study concludes that using the neutrosophic set with deep learning models may be an encouraging transition to achieve better testing accuracy, especially with limited COVID-19 datasets.

Khalifa Nour Eldeen M, Smarandache Florentin, Manogaran Gunasekaran, Loey Mohamed

2021-Jan-04

CNN, COVID-19, Coronavirus, Deep transfer learning, Neutrosophic, SARS-CoV-2

General General

DeepCNV: a deep learning approach for authenticating copy number variations.

In Briefings in bioinformatics

Copy number variations (CNVs) are an important class of variations contributing to the pathogenesis of many disease phenotypes. Detecting CNVs from genomic data remains difficult, and the most currently applied methods suffer from an unacceptably high false positive rate. A common practice is to have human experts manually review original CNV calls for filtering false positives before further downstream analysis or experimental validation. Here, we propose DeepCNV, a deep learning-based tool, intended to replace human experts when validating CNV calls, focusing on the calls made by one of the most accurate CNV callers, PennCNV. The sophistication of the deep neural network algorithm is enriched with over 10 000 expert-scored samples that are split into training and testing sets. Variant confidence, especially for CNVs, is a main roadblock impeding the progress of linking CNVs with the disease. We show that DeepCNV adds to the confidence of the CNV calls with an optimal area under the receiver operating characteristic curve of 0.909, exceeding other machine learning methods. The superiority of DeepCNV was also benchmarked and confirmed using an experimental wet-lab validation dataset. We conclude that the improvement obtained by DeepCNV results in significantly fewer false positive results and failures to replicate the CNV association results.

Glessner Joseph T, Hou Xiurui, Zhong Cheng, Zhang Jie, Khan Munir, Brand Fabian, Krawitz Peter, Sleiman Patrick M A, Hakonarson Hakon, Wei Zhi

2021-Jan-12

copy number variation, deep learning