Look at the whole X-ray, not just the bones

2006 ◽  
Vol 92 (3) ◽  
pp. 118-120
Author(s):  
S. Warwick ◽  
J. E. Smith ◽  
I. Higginson
Keyword(s):  
Early Diagnosis ◽  
Incidental Finding ◽  
X Rays ◽  
X Ray ◽  
Life Threatening

AbstractA case is presented where an incidental finding on a trauma radiograph led to early diagnosis of a potentially life-threatening tumour, highlighting the need to be vigilant when interpreting X-rays.

scholarly journals Automated segmentation and diagnosis of pneumothorax on chest X-rays with fully convolutional multi-scale ScSE-DenseNet: a retrospective study

2020 ◽  
Vol 20 (S14) ◽  
Author(s):  
Qingfeng Wang ◽  
Qiyu Liu ◽  
Guoting Luo ◽  
Zhiqin Liu ◽  
Jun Huang ◽  
...  
Keyword(s):  
Retrospective Study ◽  
Automatic Segmentation ◽  
Medical Emergency ◽  
Dice Similarity Coefficient ◽  
X Rays ◽  
X Ray ◽  
Learning Framework ◽  
Multi Scale ◽  
Life Threatening ◽  
Chest X Ray

Abstract Background Pneumothorax (PTX) may cause a life-threatening medical emergency with cardio-respiratory collapse that requires immediate intervention and rapid treatment. The screening and diagnosis of pneumothorax usually rely on chest radiographs. However, the pneumothoraces in chest X-rays may be very subtle with highly variable in shape and overlapped with the ribs or clavicles, which are often difficult to identify. Our objective was to create a large chest X-ray dataset for pneumothorax with pixel-level annotation and to train an automatic segmentation and diagnosis framework to assist radiologists to identify pneumothorax accurately and timely. Methods In this study, an end-to-end deep learning framework is proposed for the segmentation and diagnosis of pneumothorax on chest X-rays, which incorporates a fully convolutional DenseNet (FC-DenseNet) with multi-scale module and spatial and channel squeezes and excitation (scSE) modules. To further improve the precision of boundary segmentation, we propose a spatial weighted cross-entropy loss function to penalize the target, background and contour pixels with different weights. Results This retrospective study are conducted on a total of eligible 11,051 front-view chest X-ray images (5566 cases of PTX and 5485 cases of Non-PTX). The experimental results show that the proposed algorithm outperforms the five state-of-the-art segmentation algorithms in terms of mean pixel-wise accuracy (MPA) with $$0.93\pm 0.13$$ 0.93 ± 0.13 and dice similarity coefficient (DSC) with $$0.92\pm 0.14$$ 0.92 ± 0.14 , and achieves competitive performance on diagnostic accuracy with 93.45% and $$F_1$$ F 1 -score with 92.97%. Conclusion This framework provides substantial improvements for the automatic segmentation and diagnosis of pneumothorax and is expected to become a clinical application tool to help radiologists to identify pneumothorax on chest X-rays.

scholarly journals Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection Using Chest X-ray

2020 ◽  
Vol 10 (9) ◽  
pp. 3233 ◽  
Author(s):  
Tawsifur Rahman ◽  
Muhammad E. H. Chowdhury ◽  
Amith Khandakar ◽  
Khandaker R. Islam ◽  
Khandaker F. Islam ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Viral Pneumonia ◽  
Deep Convolutional Neural Network ◽  
X Rays ◽  
X Ray ◽  
Life Threatening ◽  
Chest X Ray ◽  
The Right

Pneumonia is a life-threatening disease, which occurs in the lungs caused by either bacterial or viral infection. It can be life-endangering if not acted upon at the right time and thus the early diagnosis of pneumonia is vital. The paper aims to automatically detect bacterial and viral pneumonia using digital x-ray images. It provides a detailed report on advances in accurate detection of pneumonia and then presents the methodology adopted by the authors. Four different pre-trained deep Convolutional Neural Network (CNN): AlexNet, ResNet18, DenseNet201, and SqueezeNet were used for transfer learning. A total of 5247 chest X-ray images consisting of bacterial, viral, and normal chest x-rays images were preprocessed and trained for the transfer learning-based classification task. In this study, the authors have reported three schemes of classifications: normal vs. pneumonia, bacterial vs. viral pneumonia, and normal, bacterial, and viral pneumonia. The classification accuracy of normal and pneumonia images, bacterial and viral pneumonia images, and normal, bacterial, and viral pneumonia were 98%, 95%, and 93.3%, respectively. This is the highest accuracy, in any scheme, of the accuracies reported in the literature. Therefore, the proposed study can be useful in more quickly diagnosing pneumonia by the radiologist and can help in the fast airport screening of pneumonia patients.

scholarly journals Pneumonia Detection Using Convolutional Neural Networks

2021 ◽  
Vol 7 (01) ◽  
pp. 77-80
Author(s):  
Puneet Gupta
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Large Scale ◽  
Disease Classification ◽  
World Health ◽  
X Rays ◽  
X Ray ◽  
Life Threatening ◽  
Chest X Ray ◽  
Health Organization

Abstract— Pneumonia is a life-threatening infectious disease affecting one or both lungs in humans commonly caused by bacteria called Streptococcus pneumoniae. One in three deaths in India is caused due to pneumonia as reported by World Health Organization (WHO). Chest X-Rays which are used to diagnose pneumonia, need expert radiotherapists for evaluation. Thus, developing an automatic system for detecting pneumonia would be beneficial for treating the disease without any delay particularly in remote areas. Due to the success of deep learning algorithms in analyzing medical images, Convolutional Neural Networks (CNNs) have gained much attention for disease classification. In addition, features learned by pre-trained CNN models on large-scale datasets are much useful in image classification tasks. In this work, we appraise the functionality of pre-trained CNN models utilized as feature-extractors followed by different classifiers for the classification of abnormal and normal chest X-Rays. We analytically determine the optimal CNN model for the purpose. Statistical results obtained demonstrates that pretrained CNN models employed along with supervised classifier algorithms can be very beneficial in analyzing chest X-ray images, specifically to detect Pneumonia. In this project Transfer learning and a CNN Model is used to detect whether the person has pneumonia or not using chest x-ray.

scholarly journals Lung Disease Detection Using CNN

2021 ◽  
Vol 9 (VI) ◽  
pp. 4042-4049
Author(s):  
Veeramalla Sowmya
Keyword(s):  
Neural Network ◽  
Autonomous System ◽  
Respiratory Illness ◽  
Bacterial Disease ◽  
X Rays ◽  
Streptococcus Pneumonia ◽  
X Ray ◽  
Life Threatening ◽  
Chest X Ray ◽  
Statistical Results

Covid Pneumonia is a life-threatening bacterial disease in humans that affects one or both lungs and is caused by the bacteria Streptococcus pneumonia. Also known as Covid-19, this is a respiratory illness that was first discovered in Wuhan, China. Expert radiotherapists must evaluate chest X-rays used to diagnose pneumonia. As a result, establishing an autonomous system for detecting pneumonia would be advantageous for treating the condition quickly, especially in distant places. The statistical results show that using pre trained CNN models and supervised classifier algorithms to analyse chest X-ray pictures, specifically to diagnose Pneumonia, can be highly advantageous. By constructing certain convolution neural network designs, we are developing a classifier model that accurately predicts if a person has covid or pneumonia.

scholarly journals Screening of chest X-Rays for Tuberculosis using Deep Convolutional Neural Network

2021 ◽  
Vol 9 (5) ◽  
pp. 254-258
Author(s):  
R. Rohith ◽  
S.P.Syed Ibrahim
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Medical Officer ◽  
Developing Nations ◽  
Deep Convolutional Neural Network ◽  
X Rays ◽  
X Ray ◽  
Life Threatening ◽  
Chest X Ray

Tuberculosis is a life-threatening disease that mainly affects underdeveloped as well as developing nations. While lethal it is often resistive to antibiotics and the safest way to treat a patient is to detect the disease's presence as soon as possible. Various techniques have been developed to diagnose tuberculosis and radiography of the chest is one of such methods that works well for over a decade.. Though an effective method still the success depends on the medical officer who examines the chest X-rays. Thus ,this paper proposes an approach for detecting X-ray abnormalities using deep learning. The systems output is assessed on two open Montgomery and Shenz en chest X-ray datasets and accuracy of 84 percent is achieved.

Registration Bone Scan in the Evaluation of Wrist Pain

1997 ◽  
Vol 22 (2) ◽  
pp. 161-166 ◽  
Author(s):  
A. MOHAMED ◽  
P. RYAN ◽  
M. LEWIS ◽  
J. M. JAROSZ ◽  
I. FOGELMAN ◽  
...  
Keyword(s):  
Early Diagnosis ◽  
Bone Scintigraphy ◽  
Bone Scan ◽  
Wrist Pain ◽  
X Rays ◽  
X Ray ◽  
Diagnosis And Management ◽  
Before And After

We assessed the value of bone scintigraphy combined with X-ray registration for the diagnosis and management of wrist pain in 65 patients. Studies were reported independently by two observers before and after registration. Registration improved localization of scan abnormalities in 53% (observer 1) and 61% (observer 2). In these patients, the bone scan contributed to the diagnosis independently of the X-ray in 37% and the management was altered in 31%. The value of the bone scan in the early diagnosis and management of wrist pain is increased when it is registered with X-rays.

scholarly journals An Approach to Early Diagnosis of Pneumonia on Individual Radiographs based on the CNN Information Technology

2021 ◽  
Vol 14 (1) ◽  
pp. 93-107
Author(s):  
Pavlo Radiuk ◽  
Olexander Barmak ◽  
Iurii Krak
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Information Technology ◽  
Image Analysis ◽  
Early Detection ◽  
Early Diagnosis ◽  
Clinical Decision ◽  
X Rays ◽  
X Ray ◽  
Convolution Operation

Aim: This study investigates the topology of convolutional neural networks and proposes an information technology for the early detection of pneumonia in X-rays. Background: For the past decade, pneumonia has been one of the most widespread respiratory diseases. Every year, a significant part of the world's population suffers from pneumonia, which leads to millions of deaths worldwide. Inflammation occurs rapidly and usually proceeds in severe forms. Thus, early detection of the disease plays a critical role in its successful treatment. Objective: The most operating means of diagnosing pneumonia is the chest X-ray, which produces radiographs. Automated diagnostics using computing devices and computer vision techniques have become beneficial in X-ray image analysis, serving as an ancillary decision-making system. Nonetheless, such systems require continuous improvement for individual patient adjustment to ensure a successful, timely diagnosis. Methods: Nowadays, artificial neural networks serve as a promising solution for identifying pneumonia in radiographs. Despite the high level of recognition accuracy, neural networks have been perceived as black boxes because of the unclear interpretation of their performance results. Altogether, an insufficient explanation for the early diagnosis can be perceived as a severe negative feature of automated decision-making systems, as the lack of interpretation results may negatively affect the final clinical decision. To address this issue, we propose an approach to the automated diagnosis of early pneumonia, based on the classification of radiographs with weakly expressed disease features. Results: An effective spatial convolution operation with several dilated rates, combining various receptive feature fields, was used in convolutional layers to detect and analyze visual deviations in the X-ray image. Due to applying the dilated convolution operation, the network avoids significant losses of objects' spatial information providing relatively low computational costs. We also used transfer training to overcome the lack of data in the early diagnosis of pneumonia. An image analysis strategy based on class activation maps was used to interpret the classification results, critical for clinical decision making. Conclusion: According to the computational results, the proposed convolutional architecture may be an excellent solution for instant diagnosis in case of the first suspicion of early pneumonia.

White-Light Coronal Structures: Streamers and CMEs

1994 ◽  
Vol 144 ◽  
pp. 82
Author(s):  
E. Hildner
Keyword(s):  
Emission Line ◽  
Electron Density ◽  
White Light ◽  
Coronal Mass Ejections ◽  
X Rays ◽  
Solar Cycles ◽  
Temperature Function ◽  
X Ray ◽  
Eclipse Observations ◽  
Coronal Structures

AbstractOver the last twenty years, orbiting coronagraphs have vastly increased the amount of observational material for the whitelight corona. Spanning almost two solar cycles, and augmented by ground-based K-coronameter, emission-line, and eclipse observations, these data allow us to assess,inter alia: the typical and atypical behavior of the corona; how the corona evolves on time scales from minutes to a decade; and (in some respects) the relation between photospheric, coronal, and interplanetary features. This talk will review recent results on these three topics. A remark or two will attempt to relate the whitelight corona between 1.5 and 6 R⊙to the corona seen at lower altitudes in soft X-rays (e.g., with Yohkoh). The whitelight emission depends only on integrated electron density independent of temperature, whereas the soft X-ray emission depends upon the integral of electron density squared times a temperature function. The properties of coronal mass ejections (CMEs) will be reviewed briefly and their relationships to other solar and interplanetary phenomena will be noted.

Chemical Species Identification in an SEM by Changes in Emitted X-Ray Energies

1974 ◽  
Vol 32 ◽  
pp. 570-571
Author(s):  
R. H. Duff
Keyword(s):  
Species Identification ◽  
Valence Electron ◽  
Chemical Species ◽  
X Rays ◽  
Chemical Bonds ◽  
Small Shift ◽  
X Ray ◽  
Electron Transitions ◽  
Peak Energy ◽  
Chemical Combination

A material irradiated with electrons emits x-rays having energies characteristic of the elements present. Chemical combination between elements results in a small shift of the peak energies of these characteristic x-rays because chemical bonds between different elements have different energies. The energy differences of the characteristic x-rays resulting from valence electron transitions can be used to identify the chemical species present and to obtain information about the chemical bond itself. Although these peak-energy shifts have been well known for a number of years, their use for chemical-species identification in small volumes of material was not realized until the development of the electron microprobe.

Influence of X-Ray Induced Fluorescence on Energy Dispersive X-Ray Analysis of Thin Foils

1977 ◽  
Vol 35 ◽  
pp. 328-329
Author(s):  
E. A. Kenik ◽  
J. Bentley
Keyword(s):  
Thin Foil ◽  
Electron Excitation ◽  
Simple Approach ◽  
Foil Thickness ◽  
X Rays ◽  
X Ray ◽  
Hole Spectrum ◽  
Illumination System ◽  
Thin Foils ◽  
Intensity Ratios

Cliff and Lorimer (1) have proposed a simple approach to thin foil x-ray analy sis based on the ratio of x-ray peak intensities. However, there are several experimental pitfalls which must be recognized in obtaining the desired x-ray intensities. Undesirable x-ray induced fluorescence of the specimen can result from various mechanisms and leads to x-ray intensities not characteristic of electron excitation and further results in incorrect intensity ratios.In measuring the x-ray intensity ratio for NiAl as a function of foil thickness, Zaluzec and Fraser (2) found the ratio was not constant for thicknesses where absorption could be neglected. They demonstrated that this effect originated from x-ray induced fluorescence by blocking the beam with lead foil. The primary x-rays arise in the illumination system and result in varying intensity ratios and a finite x-ray spectrum even when the specimen is not intercepting the electron beam, an ‘in-hole’ spectrum. We have developed a second technique for detecting x-ray induced fluorescence based on the magnitude of the ‘in-hole’ spectrum with different filament emission currents and condenser apertures.

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