scholarly journals CheXaid: deep learning assistance for physician diagnosis of tuberculosis using chest x-rays in patients with HIV

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Pranav Rajpurkar ◽  
Chloe O’Connell ◽  
Amit Schechter ◽  
Nishit Asnani ◽  
Jason Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Clinical Information ◽  
Hiv Positive ◽  
X Rays ◽  
Web Based ◽  
X Ray ◽  
Deep Learning Algorithm ◽  
Learning Assistance ◽  
Chest X Ray

Abstract Tuberculosis (TB) is the leading cause of preventable death in HIV-positive patients, and yet often remains undiagnosed and untreated. Chest x-ray is often used to assist in diagnosis, yet this presents additional challenges due to atypical radiographic presentation and radiologist shortages in regions where co-infection is most common. We developed a deep learning algorithm to diagnose TB using clinical information and chest x-ray images from 677 HIV-positive patients with suspected TB from two hospitals in South Africa. We then sought to determine whether the algorithm could assist clinicians in the diagnosis of TB in HIV-positive patients as a web-based diagnostic assistant. Use of the algorithm resulted in a modest but statistically significant improvement in clinician accuracy (p = 0.002), increasing the mean clinician accuracy from 0.60 (95% CI 0.57, 0.63) without assistance to 0.65 (95% CI 0.60, 0.70) with assistance. However, the accuracy of assisted clinicians was significantly lower (p < 0.001) than that of the stand-alone algorithm, which had an accuracy of 0.79 (95% CI 0.77, 0.82) on the same unseen test cases. These results suggest that deep learning assistance may improve clinician accuracy in TB diagnosis using chest x-rays, which would be valuable in settings with a high burden of HIV/TB co-infection. Moreover, the high accuracy of the stand-alone algorithm suggests a potential value particularly in settings with a scarcity of radiological expertise.

scholarly journals Evaluation of Scalability and Degree of Fine-Tuning of Deep Convolutional Neural Networks for COVID-19 Screening on Chest X-ray Images Using Explainable Deep-Learning Algorithm

2020 ◽  
Vol 10 (4) ◽  
pp. 213 ◽  
Author(s):  
Ki-Sun Lee ◽  
Jae Young Kim ◽  
Eun-tae Jeon ◽  
Won Suk Choi ◽  
Nan Hee Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Strategies ◽  
Transfer Learning ◽  
Learning Algorithm ◽  
Fine Tuning ◽  
Deep Convolutional Neural Networks ◽  
X Ray ◽  
Backbone Network ◽  
Deep Learning Algorithm ◽  
Chest X Ray

According to recent studies, patients with COVID-19 have different feature characteristics on chest X-ray (CXR) than those with other lung diseases. This study aimed at evaluating the layer depths and degree of fine-tuning on transfer learning with a deep convolutional neural network (CNN)-based COVID-19 screening in CXR to identify efficient transfer learning strategies. The CXR images used in this study were collected from publicly available repositories, and the collected images were classified into three classes: COVID-19, pneumonia, and normal. To evaluate the effect of layer depths of the same CNN architecture, CNNs called VGG-16 and VGG-19 were used as backbone networks. Then, each backbone network was trained with different degrees of fine-tuning and comparatively evaluated. The experimental results showed the highest AUC value to be 0.950 concerning COVID-19 classification in the experimental group of a fine-tuned with only 2/5 blocks of the VGG16 backbone network. In conclusion, in the classification of medical images with a limited number of data, a deeper layer depth may not guarantee better results. In addition, even if the same pre-trained CNN architecture is used, an appropriate degree of fine-tuning can help to build an efficient deep learning model.

scholarly journals Performance of a deep learning CNN model for the automated detection of 13 common conditions on Chest X-rays

2021 ◽  
Author(s):  
Tirupathi Karthik ◽  
Vijayalakshmi Kasiraman ◽  
Bhavani Paski ◽  
Kashyap Gurram ◽  
Amit Talwar ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Delayed Diagnosis ◽  
Cost Effective ◽  
Radiology Workflow ◽  
X Rays ◽  
Term Care ◽  
X Ray ◽  
Intelligence System ◽  
Deep Learning Algorithm

Background and aims: Chest X-rays are widely used, non-invasive, cost effective imaging tests. However, the complexity of interpretation and global shortage of radiologists have led to reporting backlogs, delayed diagnosis and a compromised quality of care. A fully automated, reliable artificial intelligence system that can quickly triage abnormal images for urgent radiologist review would be invaluable in the clinical setting. The aim was to develop and validate a deep learning Convoluted Neural Network algorithm to automate the detection of 13 common abnormalities found on Chest X-rays. Method: In this retrospective study, a VGG 16 deep learning model was trained on images from the Chest-ray 14, a large publicly available Chest X-ray dataset, containing over 112,120 images with annotations. Images were split into training, validation and testing sets and trained to identify 13 specific abnormalities. The primary performance measures were accuracy and precision. Results: The model demonstrated an overall accuracy of 88% in the identification of abnormal X-rays and 87% in the detection of 13 common chest conditions with no model bias. Conclusion: This study demonstrates that a well-trained deep learning algorithm can accurately identify multiple abnormalities on X-ray images. As such models get further refined, they can be used to ease radiology workflow bottlenecks and improve reporting efficiency. Napier Healthcare’s team that developed this model consists of medical IT professionals who specialize in AI and its practical application in acute & long-term care settings. This is currently being piloted in a few hospitals and diagnostic labs on a commercial basis.

scholarly journals Deep Learning Approach for COVID-19 Diagnosis Using X-Ray Images

2020 ◽  
Author(s):  
Muntasir Al-Asfoor
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Normal Subjects ◽  
Learning Approach ◽  
Computer Aided Detection ◽  
X Ray ◽  
Deep Learning Algorithm ◽  
Chest X Ray ◽  
The Times

Abstract During the times of pandemics, faster diagnosis plays a key role in the response efforts to contain the disease as well as reducing its spread. Computer-aided detection would save time and increase the quality of diagnosis in comparison with manual human diagnosis. Artificial Intelligence (AI) through deep learning is considered as a reliable method to design such systems. In this research paper, an AI based diagnosis approach has been suggested to tackle the COVID-19 pandemic. The proposed system employs a deep learning algorithm on chest x-ray images to detect the infected subjects. An enhanced Convolutional Neural Network (CNN) architecture has been designed with 22 layers which is then trained over a chest x-ray dataset. More after, a classification component has been introduced to classify the x-ray images into two categories (Covid-19 and not Covid-19) of infection. The system has been evaluated through a series of observations and experimentation. The experimental results have shown a promising performance in terms of accuracy. The system has diagnosed Covid-19 with accuracy of 95.7% and normal subjects with accuracy of 93.1 while it showed 96.7 accuracy on Pneumonia.

scholarly journals Explainable COVID-19 Detection on Chest X-rays Using an End-to-End Deep Convolutional Neural Network Architecture

2021 ◽  
Vol 5 (4) ◽  
pp. 73
Author(s):  
Mohamed Chetoui ◽  
Moulay A. Akhloufi ◽  
Bardia Yousefi ◽  
El Mostafa Bouattane
Keyword(s):  
Deep Learning ◽  
Medical Imaging ◽  
Network Architecture ◽  
Learning Algorithm ◽  
Binary Classification ◽  
X Rays ◽  
Deep Learning Algorithm ◽  
Binary Model ◽  
Chest X Ray ◽  
Multi Class Classification

The coronavirus pandemic is spreading around the world. Medical imaging modalities such as radiography play an important role in the fight against COVID-19. Deep learning (DL) techniques have been able to improve medical imaging tools and help radiologists to make clinical decisions for the diagnosis, monitoring and prognosis of different diseases. Computer-Aided Diagnostic (CAD) systems can improve work efficiency by precisely delineating infections in chest X-ray (CXR) images, thus facilitating subsequent quantification. CAD can also help automate the scanning process and reshape the workflow with minimal patient contact, providing the best protection for imaging technicians. The objective of this study is to develop a deep learning algorithm to detect COVID-19, pneumonia and normal cases on CXR images. We propose two classifications problems, (i) a binary classification to classify COVID-19 and normal cases and (ii) a multiclass classification for COVID-19, pneumonia and normal. Nine datasets and more than 3200 COVID-19 CXR images are used to assess the efficiency of the proposed technique. The model is trained on a subset of the National Institute of Health (NIH) dataset using swish activation, thus improving the training accuracy to detect COVID-19 and other pneumonia. The models are tested on eight merged datasets and on individual test sets in order to confirm the degree of generalization of the proposed algorithms. An explainability algorithm is also developed to visually show the location of the lung-infected areas detected by the model. Moreover, we provide a detailed analysis of the misclassified images. The obtained results achieve high performances with an Area Under Curve (AUC) of 0.97 for multi-class classification (COVID-19 vs. other pneumonia vs. normal) and 0.98 for the binary model (COVID-19 vs. normal). The average sensitivity and specificity are 0.97 and 0.98, respectively. The sensitivity of the COVID-19 class achieves 0.99. The results outperformed the comparable state-of-the-art models for the detection of COVID-19 on CXR images. The explainability model shows that our model is able to efficiently identify the signs of COVID-19.

COVID-DETECT: A DEEP LEARNING APPROACH FOR CLASSIFICATION OF COVID-19 PNEUMONIA FROM LUNG SEGMENTED CHEST X-RAYS

2020 ◽  
pp. 2150010
Author(s):  
S. Rajkumar ◽  
P. V. Rajaraman ◽  
Haree Shankar Meganathan ◽  
V. Sapthagirivasan ◽  
K. Tejaswinee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Rapid Screening ◽  
X Rays ◽  
X Ray ◽  
Deep Learning Algorithm ◽  
X Ray Imaging ◽  
Before And After ◽  
Wide Availability ◽  
Novel Coronavirus

The novel coronavirus (COVID-19) was first reported in the Wuhan City of China in 2019 and became a pandemic. The outbreak has caused shocking effects to the people across the globe. It is important to screen a majority of the population in every country and for the respective governments to take appropriate action. There is a need for a rapid screening system to triage and recommend the patients for appropriate treatment. Chest X-ray imaging is one of the potential modalities, which has ample advantages such as wide availability even in the villages, portability, fast data sharing option from the point of capturing to the point of investigation, etc. The aim of the proposed work is to develop a deep learning algorithm for screening COVID-19 cases by leveraging the widely available X-ray imaging. We have built a deep learning Convolutional Neural Network model utilizing a combination of the public domain (open-source COVID-19) and private data (pneumonia and normal cases). The dataset was used before and after the segmentation of the lung region for training and testing. The outcome of the classification after lung segmentation resulted in significant superiority. The average accuracy achieved by the proposed system was 96%. The heat maps incorporated in the system were helpful for our radiologists to cross-verify whether the appropriate features are identified. This system (COVID-Detect) can be used in remote places in the countries affected by COVID-19 for mass screening of suspected cases and suggesting appropriate actions, such as recommending confirmatory tests.

scholarly journals Diagnosing heart failure from chest X-ray images using deep learning

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
T Matsumoto ◽  
S Kodera ◽  
H Shinohara ◽  
A Kiyosue ◽  
Y Higashikuni ◽  
...  
Keyword(s):  
Heart Failure ◽  
Deep Learning ◽  
Data Augmentation ◽  
Learning Algorithm ◽  
Funding Source ◽  
X Rays ◽  
X Ray ◽  
Cardiothoracic Ratio ◽  
Prediction Probability ◽  
Chest X Ray

Abstract   The development of deep learning technology has enabled machines to achieve high-level accuracy in interpreting medical images. While many previous studies have examined the detection of pulmonary nodules and cardiomegaly in chest X-rays using deep learning, the application of this technology to heart failure remains rare. In this study, we investigated the performance of a deep learning algorithm in terms of diagnosing heart failure using images obtained from chest X-rays. We used 952 chest X-ray images from a labeled database published by the National Institutes of Health. Two cardiologists respectively verified and relabeled these images, for a total of 260 “normal” and 378 “heart failure” images, and the remainder were discarded because they had been incorrectly labeled. In this study “heart failure” was defined as “cardiomegaly or congestion”, in a chest X-ray with cardiothoracic ratio (CTR) over 50% or radiographic presence of pulmonary edema. To enable the machine to extract a sufficient number of features from the images, we used the general machine learning approach called data augmentation and transfer learning. Owing mostly to this technique and the adequate relabeling process, we established a model to detect heart failure in chest X-ray by applying deep learning, and obtained an accuracy of 82%. Sensitivity and specificity to heart failure were 75% and 94.4%, respectively. Furthermore, heatmap imaging allowed us to visualize decisions made by the machine. The figure shows randomly selected examples of the prediction probabilities and heatmaps of the chest X-rays from the dataset. The original image is on the left and its heatmap is on the right, with its prediction probability written below. The red areas on the heatmaps show important regions, according to which the machine determined the classification. While some images with ambiguous radiolucency such as (e) and (f) were prone to be misdiagnosed by this model, most of the images like (a)–(d) were diagnosed correctly. Deep learning can thus help support the diagnosis of heart failure using chest X-ray images. Heatmaps and probabilities of prediction Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): JSPS KAKENHI

scholarly journals COVID-19 Detection Using Deep Learning Algorithm on Chest X-ray Images

Biology ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1174
Author(s):  
Shamima Akter ◽  
F. M. Javed Mehedi Shamrat ◽  
Sovon Chakraborty ◽  
Asif Karim ◽  
Sami Azam
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Statistical Significance ◽  
Confusion Matrix ◽  
Original Data ◽  
Rank Test ◽  
X Rays ◽  
X Ray ◽  
Professional Exposure ◽  
Chest X Ray

COVID-19, regarded as the deadliest virus of the 21st century, has claimed the lives of millions of people around the globe in less than two years. Since the virus initially affects the lungs of patients, X-ray imaging of the chest is helpful for effective diagnosis. Any method for automatic, reliable, and accurate screening of COVID-19 infection would be beneficial for rapid detection and reducing medical or healthcare professional exposure to the virus. In the past, Convolutional Neural Networks (CNNs) proved to be quite successful in the classification of medical images. In this study, an automatic deep learning classification method for detecting COVID-19 from chest X-ray images is suggested using a CNN. A dataset consisting of 3616 COVID-19 chest X-ray images and 10,192 healthy chest X-ray images was used. The original data were then augmented to increase the data sample to 26,000 COVID-19 and 26,000 healthy X-ray images. The dataset was enhanced using histogram equalization, spectrum, grays, cyan and normalized with NCLAHE before being applied to CNN models. Initially using the dataset, the symptoms of COVID-19 were detected by employing eleven existing CNN models; VGG16, VGG19, MobileNetV2, InceptionV3, NFNet, ResNet50, ResNet101, DenseNet, EfficientNetB7, AlexNet, and GoogLeNet. From the models, MobileNetV2 was selected for further modification to obtain a higher accuracy of COVID-19 detection. Performance evaluation of the models was demonstrated using a confusion matrix. It was observed that the modified MobileNetV2 model proposed in the study gave the highest accuracy of 98% in classifying COVID-19 and healthy chest X-rays among all the implemented CNN models. The second-best performance was achieved from the pre-trained MobileNetV2 with an accuracy of 97%, followed by VGG19 and ResNet101 with 95% accuracy for both the models. The study compares the compilation time of the models. The proposed model required the least compilation time with 2 h, 50 min and 21 s. Finally, the Wilcoxon signed-rank test was performed to test the statistical significance. The results suggest that the proposed method can efficiently identify the symptoms of infection from chest X-ray images better than existing methods.

scholarly journals COVID-19 Pneumonia Detection Using Optimized Deep Learning Techniques

Diagnostics ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1972
Author(s):  
Abul Bashar ◽  
Ghazanfar Latif ◽  
Ghassen Ben Brahim ◽  
Nazeeruddin Mohammad ◽  
Jaafar Alghazo
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Learning Algorithm ◽  
The Body ◽  
X Rays ◽  
X Ray ◽  
Clinical Observations ◽  
Dataset Size ◽  
Chest X Ray

It became apparent that mankind has to learn to live with and adapt to COVID-19, especially because the developed vaccines thus far do not prevent the infection but rather just reduce the severity of the symptoms. The manual classification and diagnosis of COVID-19 pneumonia requires specialized personnel and is time consuming and very costly. On the other hand, automatic diagnosis would allow for real-time diagnosis without human intervention resulting in reduced costs. Therefore, the objective of this research is to propose a novel optimized Deep Learning (DL) approach for the automatic classification and diagnosis of COVID-19 pneumonia using X-ray images. For this purpose, a publicly available dataset of chest X-rays on Kaggle was used in this study. The dataset was developed over three stages in a quest to have a unified COVID-19 entities dataset available for researchers. The dataset consists of 21,165 anterior-to-posterior and posterior-to-anterior chest X-ray images classified as: Normal (48%), COVID-19 (17%), Lung Opacity (28%) and Viral Pneumonia (6%). Data Augmentation was also applied to increase the dataset size to enhance the reliability of results by preventing overfitting. An optimized DL approach is implemented in which chest X-ray images go through a three-stage process. Image Enhancement is performed in the first stage, followed by Data Augmentation stage and in the final stage the results are fed to the Transfer Learning algorithms (AlexNet, GoogleNet, VGG16, VGG19, and DenseNet) where the images are classified and diagnosed. Extensive experiments were performed under various scenarios, which led to achieving the highest classification accuracy of 95.63% through the application of VGG16 transfer learning algorithm on the augmented enhanced dataset with freeze weights. This accuracy was found to be better as compared to the results reported by other methods in the recent literature. Thus, the proposed approach proved superior in performance as compared with that of other similar approaches in the extant literature, and it made a valuable contribution to the body of knowledge. Although the results achieved so far are promising, further work is planned to correlate the results of the proposed approach with clinical observations to further enhance the efficiency and accuracy of COVID-19 diagnosis.

scholarly journals Performance of a deep learning CNN model for the automated detection of 13 common conditions on Chest X-rays

2021 ◽  
Author(s):  
Tirupathi Karthik ◽  
Vijayalakshmi Kasiraman ◽  
Bhavani Paski ◽  
Kashyap Gurram ◽  
Amit Talwar ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Delayed Diagnosis ◽  
Cost Effective ◽  
Radiology Workflow ◽  
X Rays ◽  
Term Care ◽  
X Ray ◽  
Intelligence System ◽  
Deep Learning Algorithm

Background and aims: Chest X-rays are widely used, non-invasive, cost effective imaging tests. However, the complexity of interpretation and global shortage of radiologists have led to reporting backlogs, delayed diagnosis and a compromised quality of care. A fully automated, reliable artificial intelligence system that can quickly triage abnormal images for urgent radiologist review would be invaluable in the clinical setting. The aim was to develop and validate a deep learning Convoluted Neural Network algorithm to automate the detection of 13 common abnormalities found on Chest X-rays. Method: In this retrospective study, a VGG 16 deep learning model was trained on images from the Chest-ray 14, a large publicly available Chest X-ray dataset, containing over 112,120 images with annotations. Images were split into training, validation and testing sets and trained to identify 13 specific abnormalities. The primary performance measures were accuracy and precision. Results: The model demonstrated an overall accuracy of 88% in the identification of abnormal X-rays and 87% in the detection of 13 common chest conditions with no model bias. Conclusion: This study demonstrates that a well-trained deep learning algorithm can accurately identify multiple abnormalities on X-ray images. As such models get further refined, they can be used to ease radiology workflow bottlenecks and improve reporting efficiency. Napier Healthcare’s team that developed this model consists of medical IT professionals who specialize in AI and its practical application in acute & long-term care settings. This is currently being piloted in a few hospitals and diagnostic labs on a commercial basis.

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.

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