scholarly journals Searching for pneumothorax in x-ray images using autoencoded deep features

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Antonio Sze-To ◽  
Abtin Riasatian ◽  
H. R. Tizhoosh
Keyword(s):  
Visual Inspection ◽  
Majority Vote ◽  
High Accuracy ◽  
Automated Detection ◽  
Image Search ◽  
Identification Performance ◽  
X Ray ◽  
Detection Systems ◽  
Chest X Ray ◽  
Automated Search

AbstractFast diagnosis and treatment of pneumothorax, a collapsed or dropped lung, is crucial to avoid fatalities. Pneumothorax is typically detected on a chest X-ray image through visual inspection by experienced radiologists. However, the detection rate is quite low due to the complexity of visual inspection for small lung collapses. Therefore, there is an urgent need for automated detection systems to assist radiologists. Although deep learning classifiers generally deliver high accuracy levels in many applications, they may not be useful in clinical practice due to the lack of high-quality and representative labeled image sets. Alternatively, searching in the archive of past cases to find matching images may serve as a “virtual second opinion” through accessing the metadata of matched evidently diagnosed cases. To use image search as a triaging or diagnosis assistant, we must first tag all chest X-ray images with expressive identifiers, i.e., deep features. Then, given a query chest X-ray image, the majority vote among the top k retrieved images can provide a more explainable output. In this study, we searched in a repository with more than 550,000 chest X-ray images. We developed the Autoencoding Thorax Net (short AutoThorax -Net) for image search in chest radiographs. Experimental results show that image search based on AutoThorax -Net features can achieve high identification performance providing a path towards real-world deployment. We achieved 92% AUC accuracy for a semi-automated search in 194,608 images (pneumothorax and normal) and 82% AUC accuracy for fully automated search in 551,383 images (normal, pneumothorax and many other chest diseases).

scholarly journals Evolving Deep Learning Convolutional Neural Networks for Early COVID-19 Detection in Chest X-ray Images

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1002
Author(s):  
Mohammad Khishe ◽  
Fabio Caraffini ◽  
Stefan Kuhn
Keyword(s):  
Deep Learning ◽  
Early Detection ◽  
Iterative Process ◽  
High Accuracy ◽  
X Ray ◽  
Starting Point ◽  
Reliable Model ◽  
Minimum Number ◽  
Chest X Ray ◽  
Deep Learning Model

This article proposes a framework that automatically designs classifiers for the early detection of COVID-19 from chest X-ray images. To do this, our approach repeatedly makes use of a heuristic for optimisation to efficiently find the best combination of the hyperparameters of a convolutional deep learning model. The framework starts with optimising a basic convolutional neural network which represents the starting point for the evolution process. Subsequently, at most two additional convolutional layers are added, at a time, to the previous convolutional structure as a result of a further optimisation phase. Each performed phase maximises the the accuracy of the system, thus requiring training and assessment of the new model, which gets gradually deeper, with relevant COVID-19 chest X-ray images. This iterative process ends when no improvement, in terms of accuracy, is recorded. Hence, the proposed method evolves the most performing network with the minimum number of convolutional layers. In this light, we simultaneously achieve high accuracy while minimising the presence of redundant layers to guarantee a fast but reliable model. Our results show that the proposed implementation of such a framework achieves accuracy up to 99.11%, thus being particularly suitable for the early detection of COVID-19.

scholarly journals Design of a Convolution Neural Network Model to Predict COVID-19

2021 ◽  
pp. 99-105
Author(s):  
Haritha Akkineni ◽  
Lakshmi Narayana Ukoti ◽  
Venkat Sai Babu Palagani ◽  
Shaik Ijaz Ahammad ◽  
Bindu Meghana Popuri
Keyword(s):  
Neural Network ◽  
Severe Pneumonia ◽  
Respiratory Illness ◽  
High Accuracy ◽  
Blood Oxygen ◽  
Fluid Accumulation ◽  
X Ray ◽  
Life Saving ◽  
Chest X Ray ◽  
Fast Transmission

Coronavirus is a type of viral infection. There are many different kinds, and some cause disease. A newly identified coronavirus, has caused a worldwide pandemic of respiratory illness, called COVID-19. When the virus reaches the lungs, and it causes inflammation, resulting in fluid accumulation and difficulty of breathing. When fluid enters the air in the lungs where gas exchange occurs, it leads to low blood oxygen levels. This condition is termed pneumonia. There are about four to five tests which are used to identify the presence of coronavirus in humans but among them, there are only two tests which are recommended by WHO as they take less time and a low risk to identify the virus During recent times there is a fast transmission of Covid-19, And in some countries that are unable to purchase laboratory kits for testing. We aimed to present the use of Machine learning for the high-accuracy detection of Covid-19 using chest X-ray and these images are publicly available. A convolutional neural network with minimized layers is capable of detecting Covid-19 in a limited number of chest X-ray images. This model can also detect SARS, MERS and severe pneumonia using chest X-ray images has life-saving importance for both patients and doctors.

scholarly journals Deep Learning Driven Automated Detection of COVID-19 from Radiography Images: A Comparative Analysis

2020 ◽  
Author(s):  
Sejuti Rahman ◽  
Sujan Sarker ◽  
Abdullah Al Miraj ◽  
Ragib Amin Nihal ◽  
A. K. M. Nadimul Haque ◽  
...  
Keyword(s):  
Deep Learning ◽  
World Economy ◽  
Diagnostic Technique ◽  
High Accuracy ◽  
Automated Detection ◽  
Svm Classifier ◽  
X Ray ◽  
Specimen Collection ◽  
The World ◽  
Global Catastrophe

The ravage of COVID-19 is not merely limited to taking its toll with half a million fatalities. It has halted the world economy, disrupting normalcy of lives with supervening severity than any other global catastrophe of the last few decades. The majority of the vaccine discovery attempts are still on trial, making early detection and containment the only feasible redress. The existing diagnostic technique with high accuracy has the setbacks of being expensive and sophisticated, requiring skilled individuals for specimen collection and screening resulting in lower outreach. So, methods excluding direct human intervention are much sought after, and artificial intelligence-driven automated diagnosis, especially with radiography images, captures the researchers' interest. This survey marks a detailed inspection of the deep-learning-based automated detection of COVID-19 works done to date, methodical challenges along with probable solutions, and scopes of future exploration in this arena. We also provided a comparative quantitative analysis of the performance of 315 deep models in diagnosing COVID-19, Normal, and Pneumonia from x-ray images. Our results show that Densenet201 model with Quadratic SVM classifier performs the best (accuracy: 98.16\%, sensitivity: 98.93\%, specificity: 98.77\%) and maintains high accuracies in other similar architectures as well. This proves that even though radiography images might not be conclusive for radiologists, but it is so for deep learning algorithms for detecting COVID-19. We hope this extensive review will provide a comprehensive guideline for researchers in this field.

scholarly journals Pneumonia Detection using Artificial Neural Networks and Transfer Learning Model

2019 ◽  
Vol 8 (2S4) ◽  
pp. 149-151
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Transfer Learning ◽  
Learning Model ◽  
High Accuracy ◽  
Lung Region ◽  
X Ray ◽  
Otsu Thresholding ◽  
Chest X Ray ◽  
Healthy Lung

Developing a system that helps in detecting pneumonia in chest x-ray images of lungs at a high accuracy. Firstly, a raw image is being preprocessed with the help of Otsu Thresholding and an equalizer. This helps in detecting pneumonia clouds and identifying the ratio of healthy lung region to the total region minimum. The above task is determined by importing the original chest x-ray images in the dataset and then calculating the ratio. The preprocessed data is then fed into Inception V3 model that accurately predicts the percentage of how much pneumonia is spread. This helps in identifying pneumonia present in that area and helps determining the prescribed drugs to help them clear off the symptoms.

scholarly journals Construction of Detection Systems for Lung Cancer in Chest X-ray Images and Their Superior Performance

2018 ◽  
Vol 22 (3) ◽  
pp. 109-120
Author(s):  
Kazuhiro Sawa ◽  
Akihiro Tanaka ◽  
Takumi Fukunaga ◽  
Satoru Kishida
Keyword(s):  
Lung Cancer ◽  
Superior Performance ◽  
X Ray ◽  
Detection Systems ◽  
Chest X Ray

scholarly journals Study protocol and design for the assessment of paediatric pneumonia from X-ray images using deep learning

BMJ Open ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. e044461
Author(s):  
Mark GF Sun ◽  
Senjuti Saha ◽  
Syed Ahmar Shah ◽  
Saturnino Luz ◽  
Harish Nair ◽  
...  
Keyword(s):  
Deep Learning ◽  
Low Income ◽  
Majority Vote ◽  
Open Access Journal ◽  
Ethical Review ◽  
Middle Income ◽  
X Ray ◽  
Automated Method ◽  
Study Protocols ◽  
Chest X Ray

IntroductionIn low-income and middle-income countries, pneumonia remains the leading cause of illness and death in children<5 years. The recommended tool for diagnosing paediatric pneumonia is the interpretation of chest X-ray images, which is difficult to standardise and requires trained clinicians/radiologists. Current automated computational tools have primarily focused on assessing adult pneumonia and were trained on images evaluated by a single specialist. We aim to provide a computational tool using a deep-learning approach to diagnose paediatric pneumonia using X-ray images assessed by multiple specialists trained by the WHO expert X-ray image reading panel.Methods and analysisApproximately 10 000 paediatric chest X-ray images are currently being collected from an ongoing WHO-supported surveillance study in Bangladesh. Each image will be read by two trained clinicians/radiologists for the presence or absence of primary endpoint pneumonia (PEP) in each lung, as defined by the WHO. Images whose PEP labels are discordant in either lung will be reviewed by a third specialist and the final assignment will be made using a majority vote. Convolutional neural networks will be used for lung segmentation to align and scale the images to a reference, and for interpretation of the images for the presence of PEP. The model will be evaluated against an independently collected and labelled set of images from the WHO. The study outcome will be an automated method for the interpretation of chest radiographs for diagnosing paediatric pneumonia.Ethics and disseminationAll study protocols were approved by the Ethical Review Committees of the Bangladesh Institute of Child Health, Bangladesh. The study sponsor deemed it unnecessary to attain ethical approval from the Academic and Clinical Central Office for Research and Development of University of Edinburgh, UK. The study uses existing X-ray images from an ongoing WHO-coordinated surveillance. All findings will be published in an open-access journal. All X-ray labels and statistical code will be made openly available. The model and images will be made available on request.

scholarly journals Transfer learning to detect COVID-19 automatically from X-ray images, using convolutional neural networks

2020 ◽  
Author(s):  
Mundher Taresh ◽  
Ningbo Zhu ◽  
Talal Ahmed Ali Ali
Keyword(s):  
Deep Learning ◽  
Confusion Matrix ◽  
High Accuracy ◽  
Classification Model ◽  
Statistical Parameters ◽  
Data Set ◽  
X Ray ◽  
X Ray Imaging ◽  
Chest X Ray ◽  
Novel Coronavirus

AbstractNovel coronavirus pneumonia (COVID-19) is a contagious disease that has already caused thousands of deaths and infected millions of people worldwide. Thus, all technological gadgets that allow the fast detection of COVID-19 infection with high accuracy can offer help to healthcare professionals. This study is purposed to explore the effectiveness of artificial intelligence (AI) in the rapid and reliable detection of COVID-19 based on chest X-ray imaging. In this study, reliable pre-trained deep learning algorithms were applied to achieve the automatic detection of COVID-19-induced pneumonia from digital chest X-ray images.Moreover, the study aims to evaluate the performance of advanced neural architectures proposed for the classification of medical images over recent years. The data set used in the experiments involves 274 COVID-19 cases, 380 viral pneumonia, and 380 healthy cases, which was collected from the available X-ray images on public medical repositories. The confusion matrix provided a basis for testing the post-classification model. Furthermore, an open-source library PyCM* was used to support the statistical parameters. The study revealed the superiority of Model VGG16 over other models applied to conduct this research where the model performed best in terms of overall scores and based-class scores. According to the research results, deep learning with X-ray imaging is useful in the collection of critical biological markers associated with COVID-19 infection. The technique is conducive for the physicians to make a diagnosis of COVID-19 infection. Meanwhile, the high accuracy of this computer-aided diagnostic tool can significantly improve the speed and accuracy of COVID-19 diagnosis.

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