scholarly journals Survey on Cardiomegaly Detection with Enhanced X-Ray Using Deep Learning

2021 ◽  
Vol 9 (VII) ◽  
pp. 1428-1436
Author(s):  
Prof. Vidyashree K P
Keyword(s):  
Deep Learning ◽  
High Frequency ◽  
Automated System ◽  
Heart Murmur ◽  
X Ray ◽  
Cardiothoracic Ratio ◽  
Unsharp Masking ◽  
Early Stages ◽  
Blood Clots ◽  
Computerized System

Cardiomegaly is an augmented (enlarged) heart. It is not a disease merely a sign of another condition. Cardiomegaly in the early stages, which is less severe, is called mild Cardiomegaly. These complications may give rise to conditions like Blood clots, Cardiac Arrest and sudden death, Heart failure, Heart murmur. Hence, detecting these kinds of states in the early stages helps in improving medications and reduce complications. In this paper, we will present various approaches available to detect this development using Deep learning and offer an automated system to detect the presence of Cardiomegaly in a patient. For the computerized system, we are using deep learning concepts such as U-Net and VGG16 and image enhancement (image prepossessing is required) methods like Unsharp Masking, Contrast Limited Adaptive Histogram, High-Frequency Emphasis Filtering. Accurate measure of CTR (cardiothoracic ratio) calculations can effectively diagnose the presence of Cardiomegaly in a patient.

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 Artificial Intelligence-Based Diagnosis of Cardiac and Related Diseases

2020 ◽  
Vol 9 (3) ◽  
pp. 871 ◽  
Author(s):  
Muhammad Arsalan ◽  
Muhammad Owais ◽  
Tahir Mahmood ◽  
Jiho Choi ◽  
Kang Ryoung Park
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Disease Diagnosis ◽  
Montgomery County ◽  
Single Class ◽  
Lung Segmentation ◽  
X Ray ◽  
Clinical Criteria ◽  
Cardiothoracic Ratio

Automatic chest anatomy segmentation plays a key role in computer-aided disease diagnosis, such as for cardiomegaly, pleural effusion, emphysema, and pneumothorax. Among these diseases, cardiomegaly is considered a perilous disease, involving a high risk of sudden cardiac death. It can be diagnosed early by an expert medical practitioner using a chest X-Ray (CXR) analysis. The cardiothoracic ratio (CTR) and transverse cardiac diameter (TCD) are the clinical criteria used to estimate the heart size for diagnosing cardiomegaly. Manual estimation of CTR and other diseases is a time-consuming process and requires significant work by the medical expert. Cardiomegaly and related diseases can be automatically estimated by accurate anatomical semantic segmentation of CXRs using artificial intelligence. Automatic segmentation of the lungs and heart from the CXRs is considered an intensive task owing to inferior quality images and intensity variations using nonideal imaging conditions. Although there are a few deep learning-based techniques for chest anatomy segmentation, most of them only consider single class lung segmentation with deep complex architectures that require a lot of trainable parameters. To address these issues, this study presents two multiclass residual mesh-based CXR segmentation networks, X-RayNet-1 and X-RayNet-2, which are specifically designed to provide fine segmentation performance with a few trainable parameters compared to conventional deep learning schemes. The proposed methods utilize semantic segmentation to support the diagnostic procedure of related diseases. To evaluate X-RayNet-1 and X-RayNet-2, experiments were performed with a publicly available Japanese Society of Radiological Technology (JSRT) dataset for multiclass segmentation of the lungs, heart, and clavicle bones; two other publicly available datasets, Montgomery County (MC) and Shenzhen X-Ray sets (SC), were evaluated for lung segmentation. The experimental results showed that X-RayNet-1 achieved fine performance for all datasets and X-RayNet-2 achieved competitive performance with a 75% parameter reduction.

The Microstructure of Steatite (Protoenstatite)

1985 ◽  
Vol 43 ◽  
pp. 236-237
Author(s):  
W. E. Lee ◽  
A. H. Heuer
Keyword(s):  
High Frequency ◽  
Glass Ceramics ◽  
Magnesium Silicate ◽  
Beam Current ◽  
Glassy Matrix ◽  
Angle Of Incidence ◽  
X Ray ◽  
Mechanical And Electrical Properties ◽  
Argon Ions ◽  
High Temperature Polymorph

IntroductionTraditional steatite ceramics, made by firing (vitrifying) hydrous magnesium silicate, have long been used as insulators for high frequency applications due to their excellent mechanical and electrical properties. Early x-ray and optical analysis of steatites showed that they were composed largely of protoenstatite (MgSiO3) in a glassy matrix. Recent studies of enstatite-containing glass ceramics have revived interest in the polymorphism of enstatite. Three polymorphs exist, two with orthorhombic and one with monoclinic symmetry (ortho, proto and clino enstatite, respectively). Steatite ceramics are of particular interest a they contain the normally unstable high-temperature polymorph, protoenstatite.Experimental3mm diameter discs cut from steatite rods (∼10” long and 0.5” dia.) were ground, polished, dimpled, and ion-thinned to electron transparency using 6KV Argon ions at a beam current of 1 x 10-3 A and a 12° angle of incidence. The discs were coated with carbon prior to TEM examination to minimize charging effects.

scholarly journals Deep learning-based denoising for improved dose efficiency in EDX tomography of nanoparticles

Nanoscale ◽  
2021 ◽  
Author(s):  
Alexander Skorikov ◽  
Wouter Heyvaert ◽  
Wiebke Albrecht ◽  
Daan Pelt ◽  
Sara Bals
Keyword(s):  
Deep Learning ◽  
Electron Tomography ◽  
Energy Dispersive ◽  
Elemental Distribution ◽  
X Ray ◽  
Powerful Approach ◽  
Dose Efficiency

The combination of energy-dispersive X-ray spectroscopy (EDX) and electron tomography is a powerful approach to retrieve the 3D elemental distribution in nanomaterials, providing an unprecedented level of information for complex,...

scholarly journals Integrating Machine/Deep Learning Methods and Filtering Techniques for Reliable Mineral Phase Segmentation of 3D X-ray Computed Tomography Images

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4595
Author(s):  
Parisa Asadi ◽  
Lauren E. Beckingham
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Random Forest ◽  
Ct Images ◽  
Ct Imaging ◽  
Learning Method ◽  
Learning Methods ◽  
X Ray ◽  
Machine Learning Methods ◽  
Filtering Techniques

X-ray CT imaging provides a 3D view of a sample and is a powerful tool for investigating the internal features of porous rock. Reliable phase segmentation in these images is highly necessary but, like any other digital rock imaging technique, is time-consuming, labor-intensive, and subjective. Combining 3D X-ray CT imaging with machine learning methods that can simultaneously consider several extracted features in addition to color attenuation, is a promising and powerful method for reliable phase segmentation. Machine learning-based phase segmentation of X-ray CT images enables faster data collection and interpretation than traditional methods. This study investigates the performance of several filtering techniques with three machine learning methods and a deep learning method to assess the potential for reliable feature extraction and pixel-level phase segmentation of X-ray CT images. Features were first extracted from images using well-known filters and from the second convolutional layer of the pre-trained VGG16 architecture. Then, K-means clustering, Random Forest, and Feed Forward Artificial Neural Network methods, as well as the modified U-Net model, were applied to the extracted input features. The models’ performances were then compared and contrasted to determine the influence of the machine learning method and input features on reliable phase segmentation. The results showed considering more dimensionality has promising results and all classification algorithms result in high accuracy ranging from 0.87 to 0.94. Feature-based Random Forest demonstrated the best performance among the machine learning models, with an accuracy of 0.88 for Mancos and 0.94 for Marcellus. The U-Net model with the linear combination of focal and dice loss also performed well with an accuracy of 0.91 and 0.93 for Mancos and Marcellus, respectively. In general, considering more features provided promising and reliable segmentation results that are valuable for analyzing the composition of dense samples, such as shales, which are significant unconventional reservoirs in oil recovery.

scholarly journals Evaluation of the feasibility of explainable computer-aided detection of cardiomegaly on chest radiographs using deep learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mu Sook Lee ◽  
Yong Soo Kim ◽  
Minki Kim ◽  
Muhammad Usman ◽  
Shi Sub Byon ◽  
...  
Keyword(s):  
Deep Learning ◽  
Diagnostic Performance ◽  
Absolute Error ◽  
Training Dataset ◽  
Computer Aided Detection ◽  
Test Dataset ◽  
Cardiothoracic Ratio ◽  
Computer Aided ◽  
Chest X Ray ◽  
Public Datasets

AbstractWe examined the feasibility of explainable computer-aided detection of cardiomegaly in routine clinical practice using segmentation-based methods. Overall, 793 retrospectively acquired posterior–anterior (PA) chest X-ray images (CXRs) of 793 patients were used to train deep learning (DL) models for lung and heart segmentation. The training dataset included PA CXRs from two public datasets and in-house PA CXRs. Two fully automated segmentation-based methods using state-of-the-art DL models for lung and heart segmentation were developed. The diagnostic performance was assessed and the reliability of the automatic cardiothoracic ratio (CTR) calculation was determined using the mean absolute error and paired t-test. The effects of thoracic pathological conditions on performance were assessed using subgroup analysis. One thousand PA CXRs of 1000 patients (480 men, 520 women; mean age 63 ± 23 years) were included. The CTR values derived from the DL models and diagnostic performance exhibited excellent agreement with reference standards for the whole test dataset. Performance of segmentation-based methods differed based on thoracic conditions. When tested using CXRs with lesions obscuring heart borders, the performance was lower than that for other thoracic pathological findings. Thus, segmentation-based methods using DL could detect cardiomegaly; however, the feasibility of computer-aided detection of cardiomegaly without human intervention was limited.

scholarly journals Pneumonia Classification Using Deep Learning from Chest X-ray Images During COVID-19

2021 ◽  
Author(s):  
Abdullahi Umar Ibrahim ◽  
Mehmet Ozsoz ◽  
Sertan Serte ◽  
Fadi Al-Turjman ◽  
Polycarp Shizawaliyi Yakoi
Keyword(s):  
Deep Learning ◽  
X Ray ◽  
Chest X Ray
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