scholarly journals The Effect of Image Resolution on Automated Classification of Chest X-rays

2021 ◽  
Author(s):  
Md Inzamam Ul Haque ◽  
Abhishek K Dubey ◽  
Jacob D Hinkle
Keyword(s):  
High Resolution ◽  
Automated Analysis ◽  
Image Resolution ◽  
Free Text ◽  
X Rays ◽  
Training Time ◽  
X Ray ◽  
Radiology Reports ◽  
Chest X Ray ◽  
High Resolution Images

Deep learning models have received much attention lately for their ability to achieve expert-level performance on the accurate automated analysis of chest X-rays. Although publicly available chest X-ray datasets include high resolution images, most models are trained on reduced size images due to limitations on GPU memory and training time. As compute capability continues to advance, it will become feasible to train large convolutional neural networks on high-resolution images. This study is based on the publicly available MIMIC-CXR-JPG dataset, comprising 377,110 high resolution chest X-ray images, and provided with 14 labels to the corresponding free-text radiology reports. We find, interestingly, that tasks that require a large receptive field are better suited to downscaled input images, and we verify this qualitatively by inspecting effective receptive fields and class activation maps of trained models. Finally, we show that stacking an ensemble across resolutions outperforms each individual learner at all input resolutions while providing interpretable scale weights, suggesting that multi-scale features are crucially important to information extraction from high-resolution chest X-rays.

Development of a System that Generates Structured Reports for Chest X-ray Radiography

2010 ◽  
Vol 49 (04) ◽  
pp. 360-370 ◽  
Author(s):  
Y. Matsumura ◽  
N. Mihara ◽  
Y. Kawakami ◽  
K. Sasai ◽  
H. Takeda ◽  
...  
Keyword(s):  
Narrative Form ◽  
Free Text ◽  
X Rays ◽  
Sentence Structure ◽  
X Ray ◽  
Radiology Reports ◽  
Chest X Ray ◽  
Structured Reports ◽  
Description Framework ◽  
Resource Description

Summary Objectives: Radiology reports are typically made in narrative form; this is a barrier to the implementation of advanced applications for data analysis or a decision support. We developed a system that generates structured reports for chest x-ray radiography. Methods: Based on analyzing existing reports, we determined the fundamental sentence structure of findings as compositions of procedure, region, finding, and diagnosis. We categorized the observation objects into lung, mediastinum, bone, soft tissue, and pleura and chest wall. The terms of region, finding, and diagnosis were associated with each other. We expressed the terms and the relations between the terms using a resource description framework (RDF) and developed a reporting system based on it. The system shows a list of terms in each category, and modifiers can be entered using templates that are linked to each term. This system guides users to select terms by highlighting associated terms. Fifty chest x-rays with abnormal findings were interpreted by five radiologists and reports were made either by the system or by the free-text method. Results: The system decreased the time needed to make a report by 12.5% compared with the free-text method, and the sentences generated by the system were well concordant with those made by free-text method (F-measure = 90%). The results of the questionnaire showed that our system is applicable to radiology reports of chest x-rays in daily clinical practice. Conclusions: The method of generating structured reports for chest x-rays was feasible, because it generated almost concordant reports in shorter time compared with the free-text method.

scholarly journals Micrometer-resolution imaging using MÖNCH: towards G2-less grating interferometry

2016 ◽  
Vol 23 (6) ◽  
pp. 1462-1473 ◽  
Author(s):  
Sebastian Cartier ◽  
Matias Kagias ◽  
Anna Bergamaschi ◽  
Zhentian Wang ◽  
Roberto Dinapoli ◽  
...  
Keyword(s):  
High Resolution ◽  
Silicon Detector ◽  
Limiting Factor ◽  
X Rays ◽  
X Ray ◽  
Charge Cloud ◽  
High Resolution Images ◽  
Resolution Imaging ◽  
Hybrid Silicon ◽  
Small Pixel

MÖNCH is a 25 µm-pitch charge-integrating detector aimed at exploring the limits of current hybrid silicon detector technology. The small pixel size makes it ideal for high-resolution imaging. With an electronic noise of about 110 eV r.m.s., it opens new perspectives for many synchrotron applications where currently the detector is the limiting factor,e.g.inelastic X-ray scattering, Laue diffraction and soft X-ray or high-resolution color imaging. Due to the small pixel pitch, the charge cloud generated by absorbed X-rays is shared between neighboring pixels for most of the photons. Therefore, at low photon fluxes, interpolation algorithms can be applied to determine the absorption position of each photon with a resolution of the order of 1 µm. In this work, the characterization results of one of the MÖNCH prototypes are presented under low-flux conditions. A custom interpolation algorithm is described and applied to the data to obtain high-resolution images. Images obtained in grating interferometry experiments without the use of the absorption grating G2are shown and discussed. Perspectives for the future developments of the MÖNCH detector are also presented.

Deep Neural Network-Based Screening Model for COVID-19-Infected Patients Using Chest X-Ray Images

2020 ◽  
pp. 2151004 ◽  
Author(s):  
Dilbag Singh ◽  
Vijay Kumar ◽  
Vaishali Yadav ◽  
Manjit Kaur
Keyword(s):  
Neural Network ◽  
Performance Metrics ◽  
Automated Analysis ◽  
X Rays ◽  
X Ray ◽  
Screening Model ◽  
X Ray Imaging ◽  
Adaptive Differential Evolution ◽  
Chest X Ray ◽  
Almost All

There are limited coronavirus disease 2019 (COVID-19) testing kits, therefore, development of other diagnosis approaches is desirable. The doctors generally utilize chest X-rays and Computed Tomography (CT) scans to diagnose pneumonia, lung inflammation, abscesses, and/or enlarged lymph nodes. Since COVID-19 attacks the epithelial cells that line our respiratory tract, therefore, X-ray images are utilized in this paper, to classify the patients with infected (COVID-19 [Formula: see text]ve) and uninfected (COVID-19 [Formula: see text]ve) lungs. Almost all hospitals have X-ray imaging machines, therefore, the chest X-ray images can be used to test for COVID-19 without utilizing any kind of dedicated test kits. However, the chest X-ray-based COVID-19 classification requires a radiology expert and significant time, which is precious when COVID-19 infection is increasing at a rapid rate. Therefore, the development of an automated analysis approach is desirable to save the medical professionals’ valuable time. In this paper, a deep convolutional neural network (CNN) approach is designed and implemented. Besides, the hyper-parameters of CNN are tuned using Multi-objective Adaptive Differential Evolution (MADE). Extensive experiments are performed by considering the benchmark COVID-19 dataset. Comparative analysis reveals that the proposed technique outperforms the competitive machine learning models in terms of various performance metrics.

scholarly journals Automated COVID-19 Detection from Chest X-Ray Images : A High Resolution Network (HRNet)Approach

2020 ◽  
Author(s):  
Sifat Ahmed ◽  
Tonmoy Hossain ◽  
Oishee Bintey Hoque ◽  
Sujan Sarker ◽  
Sejuti Rahman ◽  
...  
Keyword(s):  
High Resolution ◽  
Well Being ◽  
X Rays ◽  
X Ray ◽  
Pcr Diagnosis ◽  
Chest X Ray ◽  
Novel Coronavirus ◽  
And Control ◽  
Learning Architectures ◽  
Global Population

The pandemic, originated by novel coronavirus 2019 (COVID-19), continuing its devastating effect on the health, well-being, and economy of the global population. A critical step to restrain this pandemic is the early detection of COVID-19 in the human body, to constraint the exposure and control the spread of the virus. Chest X-Rays are one of the noninvasive tools to detect this disease as the manual PCR diagnosis process is quite tedious and time-consuming. In this work, we propose an automated COVID-19 classifier, utilizing available COVID and non-COVID X-Ray datasets, along with High Resolution Network (HRNet) for feature extraction embedding with the UNet for segmentation purposes. To evaluate the proposed dataset, several baseline experiments have been performed employing numerous deep learning architectures. With extensive experiment, we got 99.26% accuracy, 98.53% sensitivity, and 98.82% specificity with HRNet which surpasses the performances of the existing models. Our proposed methodology ensures unbiased high accuracy, which increases the probability of incorporating X-Ray images into the diagnosis of the disease.

scholarly journals Fluorescent X-ray Scan Image Quality Prediction

2019 ◽  
Vol 4 (1) ◽  
pp. 13-23
Author(s):  
Peter B. Weichman ◽  
Eugene M. Lavely
Keyword(s):  
Electron Beam ◽  
High Resolution ◽  
Image Quality ◽  
Integrated Circuits ◽  
Image Resolution ◽  
Image Point ◽  
Quality Prediction ◽  
X Rays ◽  
Inversion Algorithm ◽  
X Ray

AbstractThis paper summarizes approaches to image quality prediction in support of an effort under the IARPA RAVEN program to demonstrate a non-destructive, tabletop X-ray microscope for high-resolution 3D imaging of integrated circuits (ICs). The fluorescent X-rays are generated by scanning an electron beam along an appropriately patterned target layer placed in front of the sample and are then detected after passing through the sample by a high-resolution (in both solid angle and energy) backside sensor array. The images are created by way of a model-based tomographic inversion algorithm, with image resolution depending critically on the electron beam scan density and diversity of sample orientations. We derive image quality metrics that quantify the image point spread function and noise sensitivity for any proposed experiment design. Application of these metrics will guide final system design when physical data are not yet available.

scholarly journals Labelling chest x-ray reports using an open-source NLP and ML tool for text data binary classification

2019 ◽  
Author(s):  
Sohrab Towfighi ◽  
Arnav Agarwal ◽  
Denise Y. F. Mak ◽  
Amol Verma
Keyword(s):  
Internal Medicine ◽  
Language Processing ◽  
Performance Metrics ◽  
Random Search ◽  
General Medicine ◽  
X Rays ◽  
Text Data ◽  
X Ray ◽  
Radiology Reports ◽  
Chest X Ray

AbstractThe chest x-ray is a commonly requested diagnostic test on internal medicine wards which can diagnose many acute pathologies needing intervention. We developed a natural language processing (NLP) and machine learning (ML) model to identify the presence of opacities or endotracheal intubation on chest x-rays using only the radiology report. This a preliminary report of our work and findings. Using the General Medicine Inpatient Initiative (GEMINI) dataset, housing inpatient clinical and administrative data from 7 major hospitals, we retrieved 1000 plain film radiology reports which were classified according to 4 labels by an internal medicine resident. NLP/ML models were developed to identify the following on the radiograph reports: the report is that of a chest x-ray, there is definite absence of an opacity, there is definite presence of an opacity, the report is a follow-up report with minimal details in its text, and there is an endotracheal tube in place. Our NLP/ML model development methodology included a random search of either TF-IDF or bag-of-words for vectorization along with random search of various ML models. Our Python programming scripts were made publicly available on GitHub to allow other parties to train models using their own text data. 100 randomly generated ML pipelines were compared using 10-fold cross validation on 75% of the data, while 25% of the data was left out for generalizability testing. With respect to the question of whether a chest x-ray definitely lacks an opacity, the model’s performance metrics were accuracy of 0.84, precision of 0.94, recall of 0.81, and receiver operating characteristic area under curve of 0.86. Model performance was worse when trained against a highly imbalanced dataset despite the use of an advanced oversampling technique.

scholarly journals Automated COVID-19 Detection from Chest X-Ray Images : A High Resolution Network (HRNet) Approach 

2020 ◽  
Author(s):  
Sifat Ahmed ◽  
Tonmoy Hossain ◽  
Oishee Bintey Hoque ◽  
Sujan Sarker ◽  
Sejuti Rahman ◽  
...  
Keyword(s):  
High Resolution ◽  
Well Being ◽  
X Rays ◽  
X Ray ◽  
Non Invasive ◽  
Pcr Diagnosis ◽  
Chest X Ray ◽  
Novel Coronavirus ◽  
And Control ◽  
Learning Architectures

Abstract Background/ introduction: The pandemic, originated by novel coronavirus 2019 (COVID-19), continuing its devastating effect on the health, well-being, and economy of the global population. A critical step to restrain this pandemic is the early detection of COVID-19 in the human body to constraint the exposure and control the spread of the virus. Chest X-Rays are one of the non-invasive tools to detect this disease as the manual PCR diagnosis process is quite tedious and time-consuming. Our intensive background studies show that, the works till now are not efficient to produce an unbiased detection result.Method: In this work, we propose an automated COVID-19 classification method, utilizing available COVID and non-COVID X-Ray datasets, along with High Resolution Network (HRNet) for feature extraction embedding with the UNet for segmentation purposes.Results: To evaluate the proposed method, several baseline experiments have been performed employing numerous deep learning architectures. With extensive experiment, we got a significant result of 99.26% accuracy, 98.53% sensitivity, and 98.82% specificity with HRNet which surpasses the performances of the existing models.Conclusions: Finally, we conclude that our proposed methodology ensures unbiased high accuracy, which increases the probability of incorporating X-Ray images into the diagnosis of the disease.

scholarly journals An X-ray CT study of miniature clay sample preparation techniques

2019 ◽  
Vol 92 ◽  
pp. 01004
Author(s):  
Christopher Ibeh ◽  
Matteo Pedrotti ◽  
Alessandro Tarantino ◽  
Rebecca Lunn
Keyword(s):  
Sample Preparation ◽  
Shear Plane ◽  
Cohesive Soil ◽  
Image Resolution ◽  
Particle Orientation ◽  
X Ray ◽  
High Resolution Images ◽  
X Ray Computed ◽  
Sample Disturbance ◽  
Relationship Of

The quality and reliability of cohesive soil laboratory test data can be significantlyaffected by sample disturbance during sampling or sample preparation. Sample disturbance may affect key design and modelling parameters such as stiffness, preconsolidation stress, compressibility and undrained shear strength, and ultimately determine particle mobilization and shear plane development. The use of X-ray computed tomography (X-CT) in the study of soil is restricted by the inverse relationship of specimen size and obtainable image resolution. This has led to the testing of miniature specimen sizes which are far less than conventional laboratory sample size in a bid to obtain high resolution images and detailed particle-scale soil properties; however, these miniature soil specimens are more prone to sample disturbance. In this work 2% muscovite was mixed with speswhite kaolin clay as a strain marker for use in X-CT. The clay soil sample was prepared from slurry and either consolidated using an oedometer or a gypsum mould. Specimens obtained from a 7 mm tube sampler were compared to lathe trimmed specimens with a diameter (Ø) of 7 mm. Results from X-CT imaging were used to study the influence of sampler type on specimen disturbance, by analysing the muscovite particle orientation of the obtained 3D images. The results show that; for samples subjected to large consolidation stress (>200kpa) lathe trimmed specimens may be subject to lesser disturbance compared to tube sampled specimens.

Spontaneous Pneumomediastinum: Uncommon Cause of Air Leak in Late Preterm Neonate

2021 ◽  
Vol 35 (2) ◽  
pp. 93-94
Author(s):  
Jyotsna Bhushan ◽  
Shagufta Iqbal ◽  
Abhishek Chopra
Keyword(s):  
Respiratory Distress ◽  
Conservative Management ◽  
Preterm Neonate ◽  
Late Preterm ◽  
Intravenous Fluids ◽  
X Rays ◽  
Blood Gas ◽  
X Ray ◽  
Spontaneous Pneumomediastinum ◽  
Chest X Ray

A clinical case report of spontaneous pneumomediastinum in a late-preterm neonate, chest x-ray showing classical “spinnaker sail sign,” which was managed conservatively and had excellent prognosis on conservative management. Respiratory distress in a preterm neonate is a common clinical finding. Common causes include respiratory distress syndrome, transient tachypnea of the newborn, pneumonia, and pneumothorax. Pneumomediastinum is not very common cause of respiratory distress and more so spontaneous pneumomediastinum. We report here a preterm neonate with spontaneous pneumomediastinum who had excellent clinical recovery with conservative management. A male baby was delivered to G3P1A1 mother at 34 + 6 weeks through caesarean section done due to abruptio placenta. Apgar scores were 8 and 9. Maternal antenatal history was uneventful and there were no risk factors for early onset sepsis. Baby had respiratory distress soon after birth with Silverman score being 2/10. Baby was started on oxygen (O2) by nasal prongs through blender 0.5 l/min, FiO2 25%, and intravenous fluids. Blood gas done was normal. Possibility of transient tachypnea of newborn or mild hyaline membrane disease was kept. Respiratory distress increased at 20 h of life (Silverman score: 5), urgent chest x-ray done revealed “spinnaker sign” suggestive of pneumomediastinum, so baby was shifted to O2 by hood with FiO2 being 70%. Blood gas repeated was normal. Baby was managed conservatively on intravenous fluids and O2 by hood. Baby was gradually weaned off from O2 over next 5 days. As respiratory distress decreased, baby was started on orogastric feed, which baby tolerated well and then was switched to oral feeds. Serial x-rays showed resolution of pneumomediastinum. Baby was discharged on day 7 of life in stable condition on breast feeds and room air.

scholarly journals Optimization of X-ray Investigations in Dentistry Using Optical Coherence Tomography

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4554
Author(s):  
Ralph-Alexandru Erdelyi ◽  
Virgil-Florin Duma ◽  
Cosmin Sinescu ◽  
George Mihai Dobre ◽  
Adrian Bradu ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Radiation Dose ◽  
Imaging Techniques ◽  
Image Resolution ◽  
Optical Coherence ◽  
X Rays ◽  
High Quality ◽  
X Ray

The most common imaging technique for dental diagnoses and treatment monitoring is X-ray imaging, which evolved from the first intraoral radiographs to high-quality three-dimensional (3D) Cone Beam Computed Tomography (CBCT). Other imaging techniques have shown potential, such as Optical Coherence Tomography (OCT). We have recently reported on the boundaries of these two types of techniques, regarding. the dental fields where each one is more appropriate or where they should be both used. The aim of the present study is to explore the unique capabilities of the OCT technique to optimize X-ray units imaging (i.e., in terms of image resolution, radiation dose, or contrast). Two types of commercially available and widely used X-ray units are considered. To adjust their parameters, a protocol is developed to employ OCT images of dental conditions that are documented on high (i.e., less than 10 μm) resolution OCT images (both B-scans/cross sections and 3D reconstructions) but are hardly identified on the 200 to 75 μm resolution panoramic or CBCT radiographs. The optimized calibration of the X-ray unit includes choosing appropriate values for the anode voltage and current intensity of the X-ray tube, as well as the patient’s positioning, in order to reach the highest possible X-rays resolution at a radiation dose that is safe for the patient. The optimization protocol is developed in vitro on OCT images of extracted teeth and is further applied in vivo for each type of dental investigation. Optimized radiographic results are compared with un-optimized previously performed radiographs. Also, we show that OCT can permit a rigorous comparison between two (types of) X-ray units. In conclusion, high-quality dental images are possible using low radiation doses if an optimized protocol, developed using OCT, is applied for each type of dental investigation. Also, there are situations when the X-ray technology has drawbacks for dental diagnosis or treatment assessment. In such situations, OCT proves capable to provide qualitative images.

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