Potential of deep learning in assessing pneumoconiosis depicted on digital chest radiography

2020 ◽  
Vol 77 (9) ◽  
pp. 597-602
Author(s):  
Xiaohua Wang ◽  
Juezhao Yu ◽  
Qiao Zhu ◽  
Shuqiang Li ◽  
Zanmei Zhao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Classification Performance ◽  
X Ray ◽  
Testing Dataset ◽  
Learning Techniques ◽  
History Of ◽  
Digital Chest ◽  
Chest X Ray

ObjectivesTo investigate the potential of deep learning in assessing pneumoconiosis depicted on digital chest radiographs and to compare its performance with certified radiologists.MethodsWe retrospectively collected a dataset consisting of 1881 chest X-ray images in the form of digital radiography. These images were acquired in a screening setting on subjects who had a history of working in an environment that exposed them to harmful dust. Among these subjects, 923 were diagnosed with pneumoconiosis, and 958 were normal. To identify the subjects with pneumoconiosis, we applied a classical deep convolutional neural network (CNN) called Inception-V3 to these image sets and validated the classification performance of the trained models using the area under the receiver operating characteristic curve (AUC). In addition, we asked two certified radiologists to independently interpret the images in the testing dataset and compared their performance with the computerised scheme.ResultsThe Inception-V3 CNN architecture, which was trained on the combination of the three image sets, achieved an AUC of 0.878 (95% CI 0.811 to 0.946). The performance of the two radiologists in terms of AUC was 0.668 (95% CI 0.555 to 0.782) and 0.772 (95% CI 0.677 to 0.866), respectively. The agreement between the two readers was moderate (kappa: 0.423, p<0.001).ConclusionOur experimental results demonstrated that the deep leaning solution could achieve a relatively better performance in classification as compared with other models and the certified radiologists, suggesting the feasibility of deep learning techniques in screening pneumoconiosis.

scholarly journals An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Okeke Stephen ◽  
Mangal Sain ◽  
Uchenna Joseph Maduh ◽  
Do-Un Jeong
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Data Augmentation ◽  
Classification Performance ◽  
Learning Approaches ◽  
X Ray ◽  
Chest X Ray

This study proposes a convolutional neural network model trained from scratch to classify and detect the presence of pneumonia from a collection of chest X-ray image samples. Unlike other methods that rely solely on transfer learning approaches or traditional handcrafted techniques to achieve a remarkable classification performance, we constructed a convolutional neural network model from scratch to extract features from a given chest X-ray image and classify it to determine if a person is infected with pneumonia. This model could help mitigate the reliability and interpretability challenges often faced when dealing with medical imagery. Unlike other deep learning classification tasks with sufficient image repository, it is difficult to obtain a large amount of pneumonia dataset for this classification task; therefore, we deployed several data augmentation algorithms to improve the validation and classification accuracy of the CNN model and achieved remarkable validation accuracy.

scholarly journals Machine learning techniques as an efficient alternative diagnostic tool for COVID-19 cases

2021 ◽  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Sensitivity And Specificity ◽  
Predictive Power ◽  
Characteristic Curve ◽  
False Negative ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
X Ray ◽  
Learning Techniques

Background: The SARS-CoV-2 virus has demonstrated the weakness of many health systems worldwide, creating a saturation and lack of access to treatments. A bottleneck to fight this pandemic relates to the lack of diagnostic infrastructure for early detection of positive cases, particularly in rural and impoverished areas of developing countries. In this context, less costly and fast machine learning (ML) diagnosis-based systems are helpful. However, most of the research has focused on deep-learning techniques for diagnosis, which are computationally and technologically expensive. ML models have been mainly used as a benchmark and are not entirely explored in the existing literature on the topic of this paper. Objective: To analyze the capabilities of ML techniques (compared to deep learning) to diagnose COVID-19 cases based on X-ray images, assessing the performance of these techniques and using their predictive power for such a diagnosis. Methods: A factorial experiment was designed to establish this power with X-ray chest images of healthy, pneumonia, and COVID-19 infected patients. This design considers data-balancing methods, feature extraction approaches, different algorithms, and hyper-parameter optimization. The ML techniques were evaluated based on classification metrics, including accuracy, the area under the receiver operating characteristic curve (AUROC), F1-score, sensitivity, and specificity. Results: The design of experiment provided the mean and its confidence intervals for the predictive capability of different ML techniques, which reached AUROC values as high as 90% with suitable sensitivity and specificity. Among the learning algorithms, support vector machines and random forest performed best. The down-sampling method for unbalanced data improved the predictive power significantly for the images used in this study. Conclusions: Our investigation demonstrated that ML techniques are able to identify COVID-19 infected patients. The results provided suitable values of sensitivity and specificity, minimizing the false-positive or false-negative rates. The models were trained with significantly low computational resources, which helps to provide access and deployment in rural and impoverished areas.

scholarly journals Covid-19 Detection from Chest X-Ray Images Using Advanced Deep Learning Techniques

2022 ◽  
Vol 70 (1) ◽  
pp. 1541-1556
Author(s):  
Shubham Mahajan ◽  
Akshay Raina ◽  
Mohamed Abouhawwash ◽  
Xiao-Zhi Gao ◽  
Amit Kant Pandit
Keyword(s):  
Deep Learning ◽  
X Ray ◽  
Learning Techniques ◽  
Chest X Ray

scholarly journals Chest X-ray classification using Deep learning for automated COVID-19 screening

2020 ◽  
Author(s):  
Ankita Shelke ◽  
Madhura Inamdar ◽  
Vruddhi Shah ◽  
Amanshu Tiwari ◽  
Aafiya Hussain ◽  
...  
Keyword(s):  
Deep Learning ◽  
Early Stage ◽  
Ground Glass Opacity ◽  
Classification Model ◽  
X Rays ◽  
X Ray ◽  
The People ◽  
History Of ◽  
Chest X Ray

AbstractIn today’s world, we find ourselves struggling to fight one of the worst pandemics in the history of humanity known as COVID-2019 caused by a coronavirus. If we detect the virus at an early stage (before it enters the lower respiratory tract), the patient can be treated quickly. Once the virus reaches the lungs, we observe ground-glass opacity in the chest X-ray due to fibrosis in the lungs. Due to the significant differences between X-ray images of an infected and non-infected person, artificial intelligence techniques can be used to identify the presence and severity of the infection. We propose a classification model that can analyze the chest X-rays and help in the accurate diagnosis of COVID-19. Our methodology classifies the chest X-rays into 4 classes viz. normal, pneumonia, tuberculosis (TB), and COVID-19. Further, the X-rays indicating COVID-19 are classified on severity-basis into mild, medium, and severe. The deep learning model used for the classification of pneumonia, TB, and normal is VGG16 with an accuracy of 95.9 %. For the segregation of normal pneumonia and COVID-19, the DenseNet-161 was used with an accuracy of 98.9 %. ResNet-18 worked best for severity classification achieving accuracy up to 76 %. Our approach allows mass screening of the people using X-rays as a primary validation for COVID-19.

Abstract 13157: Deep Learning for Detection of Elevated Pulmonary Artery Wedge Pressure Using Standard Chest X-ray

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Yukina Hirata ◽  
Kenya Kusunose ◽  
Hirotsugu Yamada ◽  
Takumasa Tsuji ◽  
Kohei Fujimori ◽  
...  
Keyword(s):  
Heart Failure ◽  
Deep Learning ◽  
Characteristic Curve ◽  
Objective Evaluation ◽  
Left Ventricular ◽  
Training Dataset ◽  
Heart Catheterization ◽  
X Ray ◽  
Chest X Ray ◽  
Wedge Pressure

Introduction: Chest X-ray (CXR) is a useful and economical modality for the detection of congestive heart failure. However, the accuracy is limited by the subjective nature of its interpretation. Deep learning (DL) can be used to recognize diseases or findings objectively in various imaging modalities, and may outperform previous diagnostic techniques. Hypothesis: We hypothesized that DL-based analysis of CXR detect the presence of elevated pulmonary arterial wedge pressure (PAWP) in patients with suspected heart failure. Methods: We enrolled 1,013 patients with paired right heart catheterization and CXR performed from October 2009 to February 2020 in our hospital. DL algorithm for the detection of elevated PAWP was developed using the training dataset, based on a single CXR image. Independent evaluation cohort of 115 patients was performed using CXR-based DL model and echocardiographic data to detect the presence of high PAWP. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of the DL-based models compared with echocardiographic data. Results: The study included 1,013 patients (mean age, 67±13 years; 569 males [56%]). The mean PAWP was 12.5±6.4 mmHg and 218 patients (22%) had more than 18mmHg. To detect high PAWP, the AUC produced by DL algorithms was effective, and the DL algorithm with the largest AUC was ResNet50. In an evaluation cohort, to detect high PAWP, the AUC using the DL model with CXR was similar to the AUC produced by the echocardiographic left ventricular diastolic dysfunction algorithm (0.77 vs. 0.70; respectively; p=0.27), and significantly higher than the AUC by measurements of echocardiographic parameters (ResNet50 vs. other parameters; all compared p <0.05) (Figure) . Conclusions: The present results demonstrated that DL based on analysis of CXR can detect the presence of high PAWP. This finding suggests that the DL based approach may support an objective evaluation of CXR in the clinical setting.

scholarly journals COVID-19 Detection from Chest X-ray Images Using Feature Fusion and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1480
Author(s):  
Nur-A-Alam Alam ◽  
Mominul Ahsan ◽  
Md. Abdul Based ◽  
Julfikar Haider ◽  
Marcin Kowalski
Keyword(s):  
Deep Learning ◽  
Cross Validation ◽  
Feature Fusion ◽  
Extraction Methods ◽  
Classification Performance ◽  
Classification Model ◽  
Edge Preservation ◽  
X Ray ◽  
Testing Stage ◽  
Chest X Ray

Currently, COVID-19 is considered to be the most dangerous and deadly disease for the human body caused by the novel coronavirus. In December 2019, the coronavirus spread rapidly around the world, thought to be originated from Wuhan in China and is responsible for a large number of deaths. Earlier detection of the COVID-19 through accurate diagnosis, particularly for the cases with no obvious symptoms, may decrease the patient’s death rate. Chest X-ray images are primarily used for the diagnosis of this disease. This research has proposed a machine vision approach to detect COVID-19 from the chest X-ray images. The features extracted by the histogram-oriented gradient (HOG) and convolutional neural network (CNN) from X-ray images were fused to develop the classification model through training by CNN (VGGNet). Modified anisotropic diffusion filtering (MADF) technique was employed for better edge preservation and reduced noise from the images. A watershed segmentation algorithm was used in order to mark the significant fracture region in the input X-ray images. The testing stage considered generalized data for performance evaluation of the model. Cross-validation analysis revealed that a 5-fold strategy could successfully impair the overfitting problem. This proposed feature fusion using the deep learning technique assured a satisfactory performance in terms of identifying COVID-19 compared to the immediate, relevant works with a testing accuracy of 99.49%, specificity of 95.7% and sensitivity of 93.65%. When compared to other classification techniques, such as ANN, KNN, and SVM, the CNN technique used in this study showed better classification performance. K-fold cross-validation demonstrated that the proposed feature fusion technique (98.36%) provided higher accuracy than the individual feature extraction methods, such as HOG (87.34%) or CNN (93.64%).

scholarly journals A Novel Method to Identify Pneumonia through Analyzing Chest Radiographs Employing a Multichannel Convolutional Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3482
Author(s):  
Abdullah-Al Nahid ◽  
Niloy Sikder ◽  
Anupam Kumar Bairagi ◽  
Md. Abdur Razzaque ◽  
Mehedi Masud ◽  
...  
Keyword(s):  
Deep Learning ◽  
Early Stage ◽  
Machine Learning Techniques ◽  
Past Century ◽  
Radiographic Images ◽  
X Ray ◽  
The Past ◽  
Learning Techniques ◽  
Chest X Ray ◽  
Pneumonia Diagnosis

Pneumonia is a virulent disease that causes the death of millions of people around the world. Every year it kills more children than malaria, AIDS, and measles combined and it accounts for approximately one in five child-deaths worldwide. The invention of antibiotics and vaccines in the past century has notably increased the survival rate of Pneumonia patients. Currently, the primary challenge is to detect the disease at an early stage and determine its type to initiate the appropriate treatment. Usually, a trained physician or a radiologist undertakes the task of diagnosing Pneumonia by examining the patient’s chest X-ray. However, the number of such trained individuals is nominal when compared to the 450 million people who get affected by Pneumonia every year. Fortunately, this challenge can be met by introducing modern computers and improved Machine Learning techniques in Pneumonia diagnosis. Researchers have been trying to develop a method to automatically detect Pneumonia using machines by analyzing and the symptoms of the disease and chest radiographic images of the patients for the past two decades. However, with the development of cogent Deep Learning algorithms, the formation of such an automatic system is very much within the realms of possibility. In this paper, a novel diagnostic method has been proposed while using Image Processing and Deep Learning techniques that are based on chest X-ray images to detect Pneumonia. The method has been tested on a widely used chest radiography dataset, and the obtained results indicate that the model is very much potent to be employed in an automatic Pneumonia diagnosis scheme.

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