A deep-learning system to classify lung X-ray images into normal/pneumonia class

The COVID-19 pandemic caused by the new coronavirus SARS-CoV-2 has changed the world as we know it. An early diagnosis is crucial in order to prevent new outbreaks and control its rapid spread. Medical imaging techniques, such as X-ray or chest computed tomography, are commonly used for this purpose due to their reliability for COVID-19 diagnosis. Computer-aided diagnosis systems could play an essential role in aiding radiologists in the screening process. In this work, a novel Deep Learning-based system, called COVID-XNet, is presented for COVID-19 diagnosis in chest X-ray images. The proposed system performs a set of preprocessing algorithms to the input images for variability reduction and contrast enhancement, which are then fed to a custom Convolutional Neural Network in order to extract relevant features and perform the classification between COVID-19 and normal cases. The system is trained and validated using a 5-fold cross-validation scheme, achieving an average accuracy of 94.43% and an AUC of 0.988. The output of the system can be visualized using Class Activation Maps, highlighting the main findings for COVID-19 in X-ray images. These promising results indicate that COVID-XNet could be used as a tool to aid radiologists and contribute to the fight against COVID-19.

Download Full-text

Multi Deep Learning to Diagnose COVID-19 in Lung X-Ray Images with Majority Vote Technique

International Journal of Intelligent Engineering and Systems ◽

10.22266/ijies2020.1231.49 ◽

2020 ◽

Vol 13 (6) ◽

pp. 560-568

Author(s):

Ike Fibriani ◽

◽

Widjonarko Widjonarko ◽

Aris Prasetyo ◽

Angga Raharjo ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Health System ◽

Majority Vote ◽

Learning System ◽

Final Decision ◽

Initial Screening ◽

X Ray ◽

Rate Of Spread ◽

Proposed Model

The COVID-19 pandemic has become the focus of world problems that need to be resolved. This is because the rate of spread is speedy and able to take down the world's health system. Therefore, many researchers are focusing their research on solving this problem by doing an initial screening on the X-Ray image of the subject's lungs. One of them is by using Deep Learning. Several articles that talk about implemented Deep Learning for classifying X-Ray images have been published. But most of them are comparing different architecture CNN (Convolutional Neural Network). In this study, the authors try to create a multi-classifier Deep Learning system that consists of nine different CNN architectures and combined with three different Majority Vote techniques. The target of this research is to maximize the performance of classification and to minimize errors because the final decision is a compilation of decisions contained in each CNN architecture. Several models of CNN are tested in this study, both the model which used Majority Vote and Conventional CNN. The results show that the proposed model achieves an accuracy value average F1-Score 0.992 and Accuracy 0.993, according to 5 K-Fold test. The best model is CNN, which used Soft Majority Vote.

Download Full-text

Towards an affordable deep learning system: automated intervertebral disc detection in x-ray images

10.1117/12.2254692 ◽

2017 ◽

Author(s):

Ruhan Sa ◽

William Owens ◽

Raymond Wiegand ◽

Vipin Chaudhary

Keyword(s):

Deep Learning ◽

Intervertebral Disc ◽

Learning System ◽

X Ray

Download Full-text

Deep Learning System for COVID-19 Diagnosis Aid Using X-ray Pulmonary Images

Applied Sciences ◽

10.3390/app10134640 ◽

2020 ◽

Vol 10 (13) ◽

pp. 4640 ◽

Cited By ~ 8

Author(s):

Javier Civit-Masot ◽

Francisco Luna-Perejón ◽

Manuel Domínguez Morales ◽

Anton Civit

Keyword(s):

Deep Learning ◽

High Sensitivity ◽

Roc Curves ◽

Learning System ◽

Invasive Technique ◽

X Ray ◽

Non Invasive ◽

Specialist Doctor ◽

High Degree ◽

Deep Learning Model

The spread of the SARS-CoV-2 virus has made the COVID-19 disease a worldwide epidemic. The most common tests to identify COVID-19 are invasive, time consuming and limited in resources. Imaging is a non-invasive technique to identify if individuals have symptoms of disease in their lungs. However, the diagnosis by this method needs to be made by a specialist doctor, which limits the mass diagnosis of the population. Image processing tools to support diagnosis reduce the load by ruling out negative cases. Advanced artificial intelligence techniques such as Deep Learning have shown high effectiveness in identifying patterns such as those that can be found in diseased tissue. This study analyzes the effectiveness of a VGG16-based Deep Learning model for the identification of pneumonia and COVID-19 using torso radiographs. Results show a high sensitivity in the identification of COVID-19, around 100%, and with a high degree of specificity, which indicates that it can be used as a screening test. AUCs on ROC curves are greater than 0.9 for all classes considered.

Download Full-text

A Deep-Learning system to classify Lung X-ray images into Normal/Pneumonia class

International Journal of Infectious Diseases ◽

10.1016/j.ijid.2020.09.543 ◽

2020 ◽

Vol 101 ◽

pp. 202-203

Author(s):

R. Baskaran ◽

B. Ajay Rajasekaran ◽

V. Rajinikanth

Keyword(s):

Deep Learning ◽

Learning System ◽

X Ray

Download Full-text

Integration of a deep learning system for automated chest x-ray interpretation in the emergency department: A proof-of-concept

Intelligence-Based Medicine ◽

10.1016/j.ibmed.2021.100039 ◽

2021 ◽

pp. 100039

Author(s):

Candelaria Mosquera ◽

Fernando Binder ◽

Facundo Nahuel Diaz ◽

Alberto Seehaus ◽

Gabriel Ducrey ◽

...

Keyword(s):

Emergency Department ◽

Deep Learning ◽

Learning System ◽

Proof Of Concept ◽

X Ray ◽

Chest X Ray

Download Full-text

Detection of Tuberculosis in Chest X-rays using U-Net Architecture

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.a4834.119119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 2514-2519

Keyword(s):

Deep Learning ◽

False Negative ◽

Learning System ◽

X Rays ◽

X Ray ◽

Intelligence System ◽

Chest X Ray ◽

Learning Architectures ◽

Aided Diagnosis ◽

Negative Detection

x-rays are the most commonly performed which are costly diagnostic imaging tests ordered by physicians. Here we are proposing an artificial intelligence system that can reliably separate normal from abnormal would be invaluable in addressing the problem of undiagnosed disease and the lack of radiologists in low-resource settings. The aim of this study is to develop and validate a deep learning system to detect chest x-ray abnormalities and hence detect Tuberculosis (TB) and to provide a tool for Computer Aided Diagnosis (CAD).In this paper by trying to explore existing systems of Image Processing and Deep learning architectures, we are trying to achieve radiologist level detection as well as lower False Negative detection of TB by using ensemble datasets and algorithms. The prototype of a WebApp is created and can be checked on https://parth-patel12.github.io where one can upload the chest x-ray which give probabilities of the chest x-ray to be normal or TB affected.

Download Full-text

COVID-DeepNet: Hybrid Multimodal Deep Learning System for Improving COVID-19 Pneumonia Detection in Chest X-ray Images

Computers Materials & Continua ◽

10.32604/cmc.2021.012955 ◽

2021 ◽

Vol 67 (2) ◽

pp. 2409-2429

Author(s):

A. S. Al-Waisy ◽

Mazin Abed Mohammed ◽

Shumoos Al-Fahdawi ◽

M. S. Maashi ◽

Begonya Garcia-Zapirain ◽

...

Keyword(s):

Deep Learning ◽

Learning System ◽

X Ray ◽

Chest X Ray

Download Full-text

A hybrid deep learning approach towards building an intelligent system for pneumonia detection in chest X-ray images

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v11i6.pp5530-5540 ◽

2021 ◽

Vol 11 (6) ◽

pp. 5530

Author(s):

Ihssan S. Masad ◽

Amin Alqudah ◽

Ali Mohammad Alqudah ◽

Sami Almashaqbeh

Keyword(s):

Deep Learning ◽

Hybrid System ◽

Nearest Neighbor ◽

Intelligent System ◽

Detection System ◽

Learning System ◽

Support Vector ◽

K Nearest Neighbor ◽

X Ray ◽

Chest X Ray

<span>Pneumonia is a major cause for the death of children. In order to overcome the subjectivity and time consumption of the traditional detection of pneumonia from chest X-ray images; this work hypothesized that a hybrid deep learning system that consists of a convolutional neural network (CNN) model with another type of classifiers will improve the performance of the detection system. Three types of classifiers (support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF) were used along with the traditional CNN classification system (Softmax) to automatically detect pneumonia from chest X-ray images. The performance of the hybrid systems was comparable to that of the traditional CNN model with Softmax in terms of accuracy, precision, and specificity; except for the RF hybrid system which had less performance than the others. On the other hand, KNN hybrid system had the best consumption time, followed by the SVM, Softmax, and lastly the RF system. However, this improvement in consumption time (up to 4 folds) was in the expense of the sensitivity. A new hybrid artificial intelligence methodology for pneumonia detection has been implemented using small-sized chest X-ray images. The novel system achieved a very efficient performance with a short classification consumption time.</span>

Download Full-text

UBNet: Deep learning-based approach for automatic X-ray image detection of pneumonia and COVID-19 patients

Journal of X-Ray Science and Technology ◽

10.3233/xst-211005 ◽

2021 ◽

pp. 1-15

Author(s):

Chomsin S. Widodo ◽

Agus Naba ◽

Muhammad M. Mahasin ◽

Yuyun Yueniwati ◽

Terawan A. Putranto ◽

...

Keyword(s):

Deep Learning ◽

System Performance ◽

Learning System ◽

Performance Indices ◽

Image Detection ◽

X Ray ◽

Chest X Ray ◽

Hierarchical Architectures ◽

Primary Standards ◽

Hierarchical Scheme

BACKGROUND: Analysis of chest X-ray images is one of the primary standards in diagnosing patients with COVID-19 and pneumonia, which is faster than using PCR Swab method. However, accuracy of using X-ray images needs to be improved. OBJECTIVE: To develop a new deep learning system of chest X-ray images and evaluate whether it can quickly and accurately detect pneumonia and COVID-19 patients. METHODS: The developed deep learning system (UBNet v3) uses three architectural hierarchies, namely first, to build an architecture containing 7 convolution layers and 3 ANN layers (UBNet v1) to classify between normal images and pneumonia images. Second, using 4 layers of convolution and 3 layers of ANN (UBNet v2) to classify between bacterial and viral pneumonia images. Third, using UBNet v1 to classify between pneumonia virus images and COVID-19 virus infected images. An open-source database with 9,250 chest X-ray images including 3,592 COVID-19 images were used in this study to train and test the developed deep learning models. RESULTS: CNN architecture with a hierarchical scheme developed in UBNet v3 using a simple architecture yielded following performance indices to detect chest X-ray images of COVID-19 patients namely, 99.6%accuracy, 99.7%precision, 99.7%sensitivity, 99.1%specificity, and F1 score of 99.74%. A desktop GUI-based monitoring and classification system supported by a simple CNN architecture can process each chest X-ray image to detect and classify COVID-19 image with an average time of 1.21 seconds. CONCLUSION: Using three hierarchical architectures in UBNet v3 improves system performance in classifying chest X-ray images of pneumonia and COVID-19 patients. A simple architecture also speeds up image processing time.

Download Full-text