An Artificial Intelligence–Based Chest X-ray Model on Human Nodule Detection Accuracy From a Multicenter Study

Abstract BackgroundChest diseases are serious health problems that threaten the lives of people. The early and accurate diagnosis of such diseases is very crucial in the success of their treatment and cure. Pneumonia is one of the most widely occurred chest diseases responsible for a high percentage of deaths especially among children. So, detection and classification of pneumonia using the non-invasive chest x-ray imaging would have a great advantage of reducing the mortality rates.ResultsThe results showed that the best input image size in this framework was 64 64 based on comparison between different sizes. Using CNN as a deep features extractor and utilizing the 10-fold methodology the propose artificial intelligence framework achieved an accuracy of 94% for SVM and 93.9% for KNN, a sensitivity of 93.33% for SVM and 93.19% for KNN and a specificity of 96.68% for SVM and 96.60% for KNN.ConclusionsIn this study, an artificial intelligence framework has been proposed for the detection and classification of pneumonia based on chest x-ray imaging with different sizes of input images. The proposed methodology used CNN for features extraction that were fed to two different types of classifiers, namely, SVM and KNN; in addition to the SoftMax classifier which is the default CNN classifier. The proposed CNN has been trained, validated, and tested using a large dataset of chest x-ray images contains in total 5852 images.

Download Full-text

Current limitations to identify covid-19 using artificial intelligence with chest x-ray imaging (part ii). The shortcut learning problem

Health and Technology ◽

10.1007/s12553-021-00609-8 ◽

2021 ◽

Author(s):

José Daniel López-Cabrera ◽

Rubén Orozco-Morales ◽

Jorge Armando Portal-Díaz ◽

Orlando Lovelle-Enríquez ◽

Marlén Pérez-Díaz

Keyword(s):

Artificial Intelligence ◽

Learning Problem ◽

X Ray ◽

X Ray Imaging ◽

Chest X Ray

Download Full-text

The Added Effect of Artificial Intelligence on Physicians’ Performance in Detecting Thoracic Pathologies on CT and Chest X-ray: A Systematic Review

Diagnostics ◽

10.3390/diagnostics11122206 ◽

2021 ◽

Vol 11 (12) ◽

pp. 2206

Author(s):

Dana Li ◽

Lea Marie Pehrson ◽

Carsten Ammitzbøl Lauridsen ◽

Lea Tøttrup ◽

Marco Fraccaro ◽

...

Keyword(s):

Artificial Intelligence ◽

Systematic Review ◽

Assessment Tool ◽

Cochrane Library ◽

Original Research ◽

Quality Assessment Tool ◽

Bias Assessment ◽

X Ray ◽

Chest X Ray ◽

Sensitivity Specificity

Our systematic review investigated the additional effect of artificial intelligence-based devices on human observers when diagnosing and/or detecting thoracic pathologies using different diagnostic imaging modalities, such as chest X-ray and CT. Peer-reviewed, original research articles from EMBASE, PubMed, Cochrane library, SCOPUS, and Web of Science were retrieved. Included articles were published within the last 20 years and used a device based on artificial intelligence (AI) technology to detect or diagnose pulmonary findings. The AI-based device had to be used in an observer test where the performance of human observers with and without addition of the device was measured as sensitivity, specificity, accuracy, AUC, or time spent on image reading. A total of 38 studies were included for final assessment. The quality assessment tool for diagnostic accuracy studies (QUADAS-2) was used for bias assessment. The average sensitivity increased from 67.8% to 74.6%; specificity from 82.2% to 85.4%; accuracy from 75.4% to 81.7%; and Area Under the ROC Curve (AUC) from 0.75 to 0.80. Generally, a faster reading time was reported when radiologists were aided by AI-based devices. Our systematic review showed that performance generally improved for the physicians when assisted by AI-based devices compared to unaided interpretation.

Download Full-text

Sensitivity and specificity of artificial intelligence with Microsoft Azure in detecting pneumothorax in emergency department: A pilot study

Hong Kong Journal of Emergency Medicine ◽

10.1177/1024907920948994 ◽

2020 ◽

pp. 102490792094899

Author(s):

Kwok Hung Alastair Lai ◽

Shu Kai Ma

Keyword(s):

Artificial Intelligence ◽

Emergency Department ◽

Sensitivity And Specificity ◽

National Institutes Of Health ◽

Dramatic Improvement ◽

X Ray ◽

Training Images ◽

Normal Chest ◽

Chest X Ray ◽

Microsoft Azure

Background: Artificial intelligence is becoming an increasingly important tool in different medical fields. This article aims to evaluate the sensitivity and specificity of artificial intelligence trained with Microsoft Azure in detecting pneumothorax. Methods: A supervised learning artificial intelligence is trained with a collection of X-ray images of pneumothorax from National Institutes of Health chest X-ray dataset online. A subset of the image dataset focused on pneumothorax is used in training. Two artificial intelligence programs are trained with different numbers of training images. After the training, a collection of pneumothorax X-ray images from patient attending emergency department is retrieved through the Clinical Data Analysis & Reporting System. In total, 115 pneumothorax patients and 60 normal inpatients are recruited. The pneumothorax chest X-ray and the resolution chest X-ray of the above patient group and a collection of normal chest X-ray from inpatients without pneumothorax will be retrieved, and these three sets of images will then undergo testing by artificial intelligence programs to give a probability of being a pneumothorax X-ray. Results: The sensitivity of artificial intelligence-one is 33.04%, and the specificity is at least 61.74%. The sensitivity of artificial intelligence-two is 46.09%, and the specificity is at least 71.30%. The dramatic improvement of 46.09% in sensitivity and improvement of 15.48% in specificity by addition of around 1000 X-ray images is encouraging. The mean improvement of AI-two over AI-one is 19.7% increase in probability difference. Conclusions: We should not rely on artificial intelligence in diagnosing pneumothorax X-ray solely by our models and more training should be expected to explore its full function.

Download Full-text

A Solitary Feature-Based Lung Nodule Detection Approach for Chest X-Ray Radiographs

IEEE Journal of Biomedical and Health Informatics ◽

10.1109/jbhi.2017.2661805 ◽

2018 ◽

Vol 22 (2) ◽

pp. 516-524 ◽

Cited By ~ 12

Author(s):

Xuechen Li ◽

Linlin Shen ◽

Suhuai Luo

Keyword(s):

Lung Nodule ◽

X Ray ◽

Lung Nodule Detection ◽

Detection Approach ◽

Nodule Detection ◽

Chest X Ray ◽

Feature Based

Download Full-text

Automatic detection of COVID-19 in chest radiographs using serially concatenated deep and handcrafted features

Journal of X-Ray Science and Technology ◽

10.3233/xst-211050 ◽

2021 ◽

pp. 1-14

Author(s):

S. Rajesh Kannan ◽

J. Sivakumar ◽

P. Ezhilarasi

Keyword(s):

Vital Role ◽

Detection Methods ◽

Occurrence Rate ◽

Detection Accuracy ◽

X Ray ◽

Contagious Diseases ◽

Disease Occurrence ◽

Chest X Ray ◽

Feature Based ◽

Serially Concatenated

Since the infectious disease occurrence rate in the human community is gradually rising due to varied reasons, appropriate diagnosis and treatments are essential to control its spread. The recently discovered COVID-19 is one of the contagious diseases, which infected numerous people globally. This contagious disease is arrested by several diagnoses and handling actions. Medical image-supported diagnosis of COVID-19 infection is an approved clinical practice. This research aims to develop a new Deep Learning Method (DLM) to detect the COVID-19 infection using the chest X-ray. The proposed work implemented two methods namely, detection of COVID-19 infection using (i) a Firefly Algorithm (FA) optimized deep-features and (ii) the combined deep and machine features optimized with FA. In this work, a 5-fold cross-validation method is engaged to train and test detection methods. The performance of this system is analyzed individually resulting in the confirmation that the deep feature-based technique helps to achieve a detection accuracy of > 92% with SVM-RBF classifier and combining deep and machine features achieves > 96% accuracy with Fine KNN classifier. In the future, this technique may have potential to play a vital role in testing and validating the X-ray images collected from patients suffering from the infection diseases.

Download Full-text

COVID-19 detection and disease progression visualization: Deep learning on chest X-rays for classification and coarse localization

10.21203/rs.3.rs-34534/v1 ◽

2020 ◽

Cited By ~ 1

Author(s):

Tahmina Zebin ◽

Shahadate Rezvy ◽

Wei Pang

Keyword(s):

Disease Progression ◽

Detection Accuracy ◽

X Rays ◽

Automated Classification ◽

X Ray ◽

Feature Extractor ◽

Chest X Ray ◽

Activation Mapping ◽

Activation Map

Abstract Chest X-rays are playing an important role in the testing and diagnosis of COVID-19 disease in the recent pandemic. However, due to the limited amount of labelled medical images, automated classification of these images for positive and negative cases remains the biggest challenge in their reliable use in diagnosis and disease progression. We applied and implemented a transfer learning pipeline for classifying COVID-19 chest X-ray images from two publicly available chest X-ray datasets {https://github.com/ieee8023/covid-chestxray-dataset},{https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia}}. The classifier effectively distinguishes inflammation in lungs due to COVID-19 and pneumonia (viral and bacterial) from the ones with no infection (normal). We have used multiple pre-trained convolutional backbones as the feature extractor and achieved an overall detection accuracy of 91.2% , 95.3%, 96.7% for the VGG16, ResNet50 and EfficientNetB0 backbones respectively. Additionally, we trained a generative adversarial framework (a cycleGAN) to generate and augment the minority COVID-19 class in our approach. For visual explanations and interpretation purposes, we visualized the regions of input that are important for predictions and a gradient class activation mapping (Grad-CAM) technique is used in the pipeline to produce a coarse localization map of the highlighted regions in the image. This activation map can be used to monitor affected lung regions during disease progression and severity stages.

Download Full-text

Artificial Intelligence Framework for Efficient Detection and Classification of Pneumonia Using Chest Radiography Images

10.21203/rs.3.rs-66836/v1 ◽

2020 ◽

Author(s):

Ali Mohammad Alqudah ◽

Shoroq Qazan ◽

Ihssan S. Masad

Keyword(s):

Artificial Intelligence ◽

Input Image ◽

Image Size ◽

X Ray ◽

Softmax Classifier ◽

Non Invasive ◽

Efficient Detection ◽

X Ray Imaging ◽

Chest X Ray

Abstract BackgroundChest diseases are serious health problems that threaten the lives of people. The early and accurate diagnosis of such diseases is very crucial in the success of their treatment and cure. Pneumonia is one of the most widely occurred chest diseases responsible for a high percentage of deaths especially among children. So, detection and classification of pneumonia using the non-invasive chest x-ray imaging would have a great advantage of reducing the mortality rates.ResultsThe results showed that the best input image size in this framework was 64 64 based on comparison between different sizes. Using CNN as a deep features extractor and utilizing the 10-fold methodology the propose artificial intelligence framework achieved an accuracy of 94% for SVM and 93.9% for KNN, a sensitivity of 93.33% for SVM and 93.19% for KNN and a specificity of 96.68% for SVM and 96.60% for KNN.ConclusionsIn this study, an artificial intelligence framework has been proposed for the detection and classification of pneumonia based on chest x-ray imaging with different sizes of input images. The proposed methodology used CNN for features extraction that were fed to two different types of classifiers, namely, SVM and KNN; in addition to the SoftMax classifier which is the default CNN classifier. The proposed CNN has been trained, validated, and tested using a large dataset of chest x-ray images contains in total 5852 images.

Download Full-text