A Novel Tuberculosis Prediction Model by Extracting Radiological Features Present in Chest X-ray Images Using Modified Discrete Grey Wolf Optimizer Based Segmentation

2021 ◽  
Vol 11 (10) ◽  
pp. 2519-2528
Author(s):  
J. Senthil Kumar ◽  
S. Appavu Alias Balamurugan ◽  
S. Sasikala
Keyword(s):  
Deep Learning ◽  
Image Interpretation ◽  
Grey Wolf Optimizer ◽  
Gray Wolf ◽  
X Rays ◽  
Grey Wolf ◽  
Data Set ◽  
X Ray ◽  
Novel Approach ◽  
Chest X Ray

In 2018, an invariant numbers ranging from 10 million people suffered from Tuberculosis (TB) approximately that has remained quite stable in recent years, based on the WHO 2019 survey report. This infection rate differs invariable among countries, from less than 5 to more than 500 new infections per 1,00,000 people each year, with a global average of around 130. Around 1.2 million HIV negative deaths existed in 2018. If this prevailing disease were diagnosed earlier, the death rate would have been under control, however sophisticated testing techniques tend to be cost prohibitive of wider acceptance. Some of the most important methods for TB diagnosis include thoracic X-ray image interpretation through image processing by the identification of various structures on thoracic X-rays and anomaly assessment is an important stage in computer-aided diagnosis systems. Chest form and size may contain indications for serious disorders such as pneumothorax, pneumoconiosis, tuberculosis and emphysema. Substantial work might have contributed to simplify diagnosis through implementing various statistical strategies to medical images, minimizing overtime and dramatically lowering overhead costs. In addition, recent advances in deep learning have provided magnificent results in the detection of images in different fields, but their use in diagnose TB remains limited. Thus, this work focuses on the development of a novel approach in disease detection. The concepts presented in this work are placed into practice and linked to current literature. We also proposed an automatic approach in conventional poster anterior chest X-rays for TB identification and diagnosis. We use the chest X-ray image with modified discrete grey wolf optimizer for segmentation techniques to eradicate abnormal areas and shape abnormality. We extract various features from the X-ray image with a shear let extraction that allows the image to be classified as normal or abnormal, based on a deep learning classifier, via the improved residual VGG net CNN with big data. Using Shenzhen Hospital Chest X-ray data set we test the efficiency of our system. The suggested technique has competitive results with comparatively shorter training period and greater precision depending on Masientropy based discrete gray wolf optimizer segmentation with an improved residual VGG net CNN. All the simulations are carried out in a mat lab environment.

scholarly journals Cardiac or Infectious? Transfer Learning with Chest X-Rays for ER Patient Classification

2020 ◽  
Author(s):  
Jonathan Stubblefield ◽  
Mitchell Hervert ◽  
Jason Causey ◽  
Jake Qualls ◽  
Wei Dong ◽  
...  
Keyword(s):  
Deep Learning ◽  
Clinical Data ◽  
Image Data ◽  
Learning Model ◽  
Patient Classification ◽  
X Rays ◽  
Data Set ◽  
X Ray ◽  
Chest X Ray ◽  
Deep Learning Model

AbstractOne of the challenges with urgent evaluation of patients with acute respiratory distress syndrome (ARDS) in the emergency room (ER) is distinguishing between cardiac vs infectious etiologies for their pulmonary findings. We evaluated ER patient classification for cardiac and infection causes with clinical data and chest X-ray image data. We show that a deep-learning model trained with an external image data set can be used to extract image features and improve the classification accuracy of a data set that does not contain enough image data to train a deep-learning model. We also conducted clinical feature importance analysis and identified the most important clinical features for ER patient classification. This model can be upgraded to include a SARS-CoV-2 specific classification with COVID-19 patients data. The current model is publicly available with an interface at the web link: http://nbttranslationalresearch.org/.Data statementThe clinical data and chest x-ray image data for this study were collected and prepared by the residents and researchers of the Joint Translational Research Lab of Arkansas State University (A-State) and St. Bernards Medical Center (SBMC) Internal Medicine Residency Program. As data collection is on-going for the project stage-II of clinical testing, raw data is not currently available for data sharing to the public.EthicsThis study was approved by the St. Bernards Medical Center’s Institutional Review Board (IRB).

scholarly journals COVID-19 Data Analysis using Chest X-ray

2021 ◽  
Vol 1 (4) ◽  
pp. 5-10
Author(s):  
Ishtiaque Ahmed ◽  
◽  
Manan Darda ◽  
Neha Tikyani ◽  
Rachit Agrawal ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Analysis ◽  
Predictive Value ◽  
Large Scale ◽  
X Rays ◽  
Learning Models ◽  
Data Set ◽  
X Ray ◽  
Dataset Size ◽  
Chest X Ray

The COVID-19 pandemic has caused large-scale outbreaks in more than 150 countries worldwide, causing massive damage to the livelihood of many people. The capacity to identify contaminated patients early and get unique treatment is quite possibly the primary stride in the battle against COVID-19. One of the quickest ways to diagnose patients is to use radiography and radiology images to detect the disease. Early studies have shown that chest X-rays of patients infected with COVID-19 have unique abnormalities. To identify COVID-19 patients from chest X-ray images, we used various deep learning models based on previous studies. We first compiled a data set of 2,815 chest radiographs from public sources. The model produces reliable and stable results with an accuracy of 91.6%, a Positive Predictive Value of 80%, a Negative Predictive Value of 100%, specificity of 87.50%, and Sensitivity of 100%. It is observed that the CNN-based architecture can diagnose COVID19 disease. The parameters’ outcomes can be further improved by increasing the dataset size and by developing the CNN-based architecture for training the model.

scholarly journals COVID-19 Data Analysis using Chest X-Ray

2021 ◽  
pp. 5-10
Author(s):  
Ishtiaque Ahmed ◽  
◽  
Manan Darda ◽  
Neha Tikyani ◽  
Rachit Agrawal ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Analysis ◽  
Predictive Value ◽  
Large Scale ◽  
X Rays ◽  
Learning Models ◽  
Data Set ◽  
X Ray ◽  
Dataset Size ◽  
Chest X Ray

The COVID-19 pandemic has caused large-scale outbreaks in more than 150 countries worldwide, causing massive damage to the livelihood of many people. The capacity to identify contaminated patients early and get unique treatment is quite possibly the primary stride in the battle against COVID-19. One of the quickest ways to diagnose patients is to use radiography and radiology images to detect the disease. Early studies have shown that chest X-rays of patients infected with COVID-19 have unique abnormalities. To identify COVID-19 patients from chest X-ray images, we used various deep learning models based on previous studies. We first compiled a data set of 2,815 chest radiographs from public sources. The model produces reliable and stable results with an accuracy of 91.6%, a Positive Predictive Value of 80%, a Negative Predictive Value of 100%, specificity of 87.50%, and Sensitivity of 100%. It is observed that the CNN-based architecture can diagnose COVID-19 disease. The parameters’ outcomes can be further improved by increasing the dataset size and by developing the CNN-based architecture for training the model.

scholarly journals Accessory pathway analysis using a multimodal deep learning model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Makoto Nishimori ◽  
Kunihiko Kiuchi ◽  
Kunihiro Nishimura ◽  
Kengo Kusano ◽  
Akihiro Yoshida ◽  
...  
Keyword(s):  
Deep Learning ◽  
Electrophysiological Study ◽  
Accessory Pathway ◽  
Learning Model ◽  
X Rays ◽  
X Ray ◽  
Wpw Syndrome ◽  
Chest X Ray ◽  
Deep Learning Model ◽  
Ecg Data

AbstractCardiac accessory pathways (APs) in Wolff–Parkinson–White (WPW) syndrome are conventionally diagnosed with decision tree algorithms; however, there are problems with clinical usage. We assessed the efficacy of the artificial intelligence model using electrocardiography (ECG) and chest X-rays to identify the location of APs. We retrospectively used ECG and chest X-rays to analyse 206 patients with WPW syndrome. Each AP location was defined by an electrophysiological study and divided into four classifications. We developed a deep learning model to classify AP locations and compared the accuracy with that of conventional algorithms. Moreover, 1519 chest X-ray samples from other datasets were used for prior learning, and the combined chest X-ray image and ECG data were put into the previous model to evaluate whether the accuracy improved. The convolutional neural network (CNN) model using ECG data was significantly more accurate than the conventional tree algorithm. In the multimodal model, which implemented input from the combined ECG and chest X-ray data, the accuracy was significantly improved. Deep learning with a combination of ECG and chest X-ray data could effectively identify the AP location, which may be a novel deep learning model for a multimodal model.

scholarly journals Detecting Coronavirus from Chest X-rays Using Transfer Learning

COVID ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 403-415
Author(s):  
Abeer Badawi ◽  
Khalid Elgazzar
Keyword(s):  
Deep Learning ◽  
Binary Classification ◽  
Healthcare Providers ◽  
Large Set ◽  
X Rays ◽  
Learning Models ◽  
X Ray ◽  
Chest X Ray ◽  
Multi Class Classification ◽  
Novel Coronavirus

Coronavirus disease (COVID-19) is an illness caused by a novel coronavirus family. One of the practical examinations for COVID-19 is chest radiography. COVID-19 infected patients show abnormalities in chest X-ray images. However, examining the chest X-rays requires a specialist with high experience. Hence, using deep learning techniques in detecting abnormalities in the X-ray images is presented commonly as a potential solution to help diagnose the disease. Numerous research has been reported on COVID-19 chest X-ray classification, but most of the previous studies have been conducted on a small set of COVID-19 X-ray images, which created an imbalanced dataset and affected the performance of the deep learning models. In this paper, we propose several image processing techniques to augment COVID-19 X-ray images to generate a large and diverse dataset to boost the performance of deep learning algorithms in detecting the virus from chest X-rays. We also propose innovative and robust deep learning models, based on DenseNet201, VGG16, and VGG19, to detect COVID-19 from a large set of chest X-ray images. A performance evaluation shows that the proposed models outperform all existing techniques to date. Our models achieved 99.62% on the binary classification and 95.48% on the multi-class classification. Based on these findings, we provide a pathway for researchers to develop enhanced models with a balanced dataset that includes the highest available COVID-19 chest X-ray images. This work is of high interest to healthcare providers, as it helps to better diagnose COVID-19 from chest X-rays in less time with higher accuracy.

scholarly journals A New Deep Learning Pipeline to Detect Covid-19 on Chest X-Ray Images using Local Binary Pattern, Dual Tree Complex Wavelet Transform and Convolutional Neural Networks

2020 ◽  
Author(s):  
Huseyin Yaşar ◽  
Murat Ceylan
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Wavelet Transform ◽  
Test Data ◽  
Local Binary Pattern ◽  
Complex Wavelet Transform ◽  
Data Set ◽  
X Ray ◽  
Chest X Ray ◽  
Sensitivity Specificity

Abstract At the end of 2019, a new type of virus, belonging to the coronaviridae family has emerged and it is considered that the virus in question is of zootonic origin. The virus that emerged in China first affected this country and then spread worldwide. Pneumonia develops due to Covid-19 virus in patients having severe disease symptoms. Many literature studies have been carried out in the process where the effects of the disease-induced pneumonia in lungs have been demonstrated with the help of chest X-ray imaging. In this study, which aims at early diagnosis of Covid-19 disease by using X-Ray images, the deep-learning approach, which is a state-of-the-art artificial intelligence method, was used and automatic classification of images was performed using Convolutional Neural Networks (CNN). In the first training-test data set used in the study, there were a total of 230 abnormal and 80 normal X-Ray images, while in the second training-test data set there were 476 X-Ray images, of which 150 abnormal and 326 normal. Thus, classification results have been provided for two data sets, containing predominantly abnormal images and predominantly normal images respectively. In the study, a 23-layer CNN architecture was developed. Within the scope of the study, results were obtained by using chest X-Ray images directly in training-test procedures and the sub-band images obtained by applying Dual Tree Complex Wavelet Transform (DT-CWT) to the above-mentioned images. The same experiments were repeated using images obtained by applying Local Binary Pattern (LBP) to the chest X-Ray images. Within the scope of the study, a new result generation algorithm having been put forward additionally, it was ensured that the experimental results were combined and the success of the study was improved. In the experiments carried out in the study, the trainings were carried out using the k-fold cross validation method. Here the k value was chosen 23. Considering the highest results of the tests performed in the study, values of sensitivity, specificity, accuracy and AUC for the first training-test data set were calculated to be 1, 1, 0,9913 and 0,9996; while for the second data set of training-test, they were 1, 0,9969, 0,9958 and 0,9996 respectively. Considering the average highest results of the experiments performed within the scope of the study, the values of sensitivity, specificity, accuracy and AUC for the first training-test data set were 0,9933, 0,9725, 0,9843 and 0,9988; while for the second training-test data set, they were 0,9813, 0,9908, 0,9857 and 0,9983 respectively.

scholarly journals An Effective Convolutional Neural Network Model for the Early Detection of COVID-19 Using Chest X-ray Images

2021 ◽  
Vol 11 (21) ◽  
pp. 10301
Author(s):  
Muhammad Shoaib Farooq ◽  
Attique Ur Rehman ◽  
Muhammad Idrees ◽  
Muhammad Ahsan Raza ◽  
Jehad Ali ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Real World ◽  
Early Stage ◽  
Binary Classification ◽  
X Rays ◽  
X Ray ◽  
Proposed Model ◽  
Chest X Ray

COVID-19 has been difficult to diagnose and treat at an early stage all over the world. The numbers of patients showing symptoms for COVID-19 have caused medical facilities at hospitals to become unavailable or overcrowded, which is a major challenge. Studies have recently allowed us to determine that COVID-19 can be diagnosed with the aid of chest X-ray images. To combat the COVID-19 outbreak, developing a deep learning (DL) based model for automated COVID-19 diagnosis on chest X-ray is beneficial. In this research, we have proposed a customized convolutional neural network (CNN) model to detect COVID-19 from chest X-ray images. The model is based on nine layers which uses a binary classification method to differentiate between COVID-19 and normal chest X-rays. It provides COVID-19 detection early so the patients can be admitted in a timely fashion. The proposed model was trained and tested on two publicly available datasets. Cross-dataset studies are used to assess the robustness in a real-world context. Six hundred X-ray images were used for training and two hundred X-rays were used for validation of the model. The X-ray images of the dataset were preprocessed to improve the results and visualized for better analysis. The developed algorithm reached 98% precision, recall and f1-score. The cross-dataset studies also demonstrate the resilience of deep learning algorithms in a real-world context with 98.5 percent accuracy. Furthermore, a comparison table was created which shows that our proposed model outperforms other relative models in terms of accuracy. The quick and high-performance of our proposed DL-based customized model identifies COVID-19 patients quickly, which is helpful in controlling the COVID-19 outbreak.

scholarly journals COVIDScreen: Explainable deep learning framework for differential diagnosis of COVID-19 using chest X-Rays

2020 ◽  
Author(s):  
Rajeev Kumar Singh ◽  
Rohan Pandey ◽  
Rishie Nandhan Babu
Keyword(s):  
Deep Learning ◽  
Model Performance ◽  
Alternative Methods ◽  
Model Complexity ◽  
X Rays ◽  
Kappa Score ◽  
X Ray ◽  
Pruning Strategy ◽  
Chest X Ray ◽  
To Come

Abstract COVID-19 has emerged as a global crisis with unprecedented socio-economic challenges, jeopardizing our lives and livelihoods for years to come. The unavailability of vaccines for COVID-19 has rendered rapid testing of the population instrumental in order to contain the exponential rise in cases of infection. Shortage of RT-PCR test kits and delay in obtaining test results calls for alternative methods of rapid and reliable diagnosis. In this article, we propose a novel Deep Learning based solution to rapidly classify COVID -19 patient using chest X-Ray. The proposed solution uses image enhancement, image segmentation and employs a modified stacked ensemble model consisting of four CNN base-learners along with Naive Bayes as meta-learner to classify Chest X-Ray into three classes viz. COVID-19, Pneumonia and Normal. An effective pruning strategy as introduced in the proposed framework results in increased model performance, generalisability, and decreased model complexity. We incorporate explainability in our article by using Grad-CAM visualisation in order to establish trust in the medical AI system. Furthermore, we evaluate multiple state of the art GAN architectures and their ability to generate realistic synthetic samples of COVID-19 chest X-Rays to deal with limited numbers of training samples. The proposed solution significantly outperforms existing methods, with 98.67\% accuracy, 0.98 Kappa score, and F-1 scores of 100, 98, and 98 for COVID-19, Normal, and Pneumonia classes respectively on standard datasets. The proposed solution can be used as one element of patient evaluation along with gold standard clinical and laboratory testing.

scholarly journals Deep Learning for Screening COVID-19 using Chest X-Ray Images

2020 ◽  
Author(s):  
Sanhita Basu ◽  
Sushmita Mitra ◽  
Nilanjan Saha
Keyword(s):  
Deep Learning ◽  
Lung Parenchyma ◽  
Clinical Findings ◽  
Data Sets ◽  
X Rays ◽  
X Ray ◽  
Chest X Ray ◽  
Increasing Demand ◽  
Novel Coronavirus ◽  
Diverse Data

AbstractWith the ever increasing demand for screening millions of prospective “novel coronavirus” or COVID-19 cases, and due to the emergence of high false negatives in the commonly used PCR tests, the necessity for probing an alternative simple screening mechanism of COVID-19 using radiological images (like chest X-Rays) assumes importance. In this scenario, machine learning (ML) and deep learning (DL) offer fast, automated, effective strategies to detect abnormalities and extract key features of the altered lung parenchyma, which may be related to specific signatures of the COVID-19 virus. However, the available COVID-19 datasets are inadequate to train deep neural networks. Therefore, we propose a new concept called domain extension transfer learning (DETL). We employ DETL, with pre-trained deep convolutional neural network, on a related large chest X-Ray dataset that is tuned for classifying between four classes viz. normal, other_disease, pneumonia and Covid — 19. A 5-fold cross validation is performed to estimate the feasibility of using chest X-Rays to diagnose COVID-19. The initial results show promise, with the possibility of replication on bigger and more diverse data sets. The overall accuracy was measured as 95.3% ± 0.02. In order to get an idea about the COVID-19 detection transparency, we employed the concept of Gradient Class Activation Map (Grad-CAM) for detecting the regions where the model paid more attention during the classification. This was found to strongly correlate with clinical findings, as validated by experts.

scholarly journals A Deep Learning Approach to Detect COVID-19 Patients from Chest X-ray Images

AI ◽  
2020 ◽  
Vol 1 (3) ◽  
pp. 418-435
Author(s):  
Khandaker Haque ◽  
Ahmed Abdelgawad
Keyword(s):  
Deep Learning ◽  
World Health ◽  
X Rays ◽  
X Ray ◽  
Global Pandemic ◽  
Proposed Model ◽  
Dental Diseases ◽  
Chest X Ray ◽  
Artery Disease ◽  
Health Organization

Deep Learning has improved multi-fold in recent years and it has been playing a great role in image classification which also includes medical imaging. Convolutional Neural Networks (CNNs) have been performing well in detecting many diseases including coronary artery disease, malaria, Alzheimer’s disease, different dental diseases, and Parkinson’s disease. Like other cases, CNN has a substantial prospect in detecting COVID-19 patients with medical images like chest X-rays and CTs. Coronavirus or COVID-19 has been declared a global pandemic by the World Health Organization (WHO). As of 8 August 2020, the total COVID-19 confirmed cases are 19.18 M and deaths are 0.716 M worldwide. Detecting Coronavirus positive patients is very important in preventing the spread of this virus. On this conquest, a CNN model is proposed to detect COVID-19 patients from chest X-ray images. Two more CNN models with different number of convolution layers and three other models based on pretrained ResNet50, VGG-16 and VGG-19 are evaluated with comparative analytical analysis. All six models are trained and validated with Dataset 1 and Dataset 2. Dataset 1 has 201 normal and 201 COVID-19 chest X-rays whereas Dataset 2 is comparatively larger with 659 normal and 295 COVID-19 chest X-ray images. The proposed model performs with an accuracy of 98.3% and a precision of 96.72% with Dataset 2. This model gives the Receiver Operating Characteristic (ROC) curve area of 0.983 and F1-score of 98.3 with Dataset 2. Moreover, this work shows a comparative analysis of how change in convolutional layers and increase in dataset affect classifying performances.

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