The exploration of feature extraction and machine learning for predicting bone density from simple spine X-ray images in a Korean population

2019 ◽  
Vol 49 (4) ◽  
pp. 613-618 ◽  
Author(s):  
Sangwoo Lee ◽  
Eun Kyung Choe ◽  
Hae Yeon Kang ◽  
Ji Won Yoon ◽  
Hua Sun Kim
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Bone Density ◽  
Korean Population ◽  
X Ray ◽  
Simple Spine

scholarly journals A Comprehensive Investigation of Machine Learning Feature Extraction and Classification Methods for Automated Diagnosis of COVID-19 Based on X-Ray Images

2021 ◽  
Vol 66 (3) ◽  
pp. 3289-3310
Author(s):  
Mazin Abed Mohammed ◽  
Karrar Hameed Abdulkareem ◽  
Begonya Garcia-Zapirain ◽  
Salama A. Mostafa ◽  
Mashael S. Maashi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Classification Methods ◽  
Automated Diagnosis ◽  
Comprehensive Investigation ◽  
X Ray

Concealed Weapon Detection using Image Processing and Machine Learning

2021 ◽  
Vol 9 (12) ◽  
pp. 1374-1384
Author(s):  
Saksham Gosain
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Feature Extraction ◽  
Image Fusion ◽  
Thermal Imaging ◽  
Facial Feature ◽  
Ray Method ◽  
X Ray ◽  
Facial Feature Extraction ◽  
Concealed Weapon Detection

Abstract: This research paper presents a study of concealed weapon detection using image processing and machine learning. In order to attempt to replace the traditional method of detecting hidden weapons i.e. x-ray method with an automated and possibly a less error prone procedure, potential alternate techniques such as neural networks and image fusion have been studied and explored to identify the best possible solution. We propose a method to fuse Thermal/IR image with the conventional RGB image or HSV image in order to reduce image noise and retain all the critical features of the image to achieve both weapon detection and facial feature extraction. Keywords: Image fusion; concealed weapon; feature extraction; neural network; thermal imaging

scholarly journals Computer Aid Screening of COVID19 using X-ray and CT Scan Images: A Comparative Study

2020 ◽  
Author(s):  
Prabira Kumar Sethy ◽  
Chanki Pandey ◽  
Santi Kumari Behera
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Ct Scan ◽  
Transfer Learning ◽  
Screening Method ◽  
Learning Approaches ◽  
X Ray ◽  
Computer Aid ◽  
Deep Feature ◽  
Deep Feature Extraction

In this article, we analyse the computer aid screening method of COVID19 using Xray and CT scan images. The main objective is to set an analytical closure about the computer aid screening of COVID19 among the X-ray image and CT scan image. The computer aid screening method includes deep feature extraction, transfer learning and traditional machine learning image classification approach. The deep feature extraction and transfer learning method considered 13 pre-trained CNN model. The machine learning approach includes three sets of features and three classifiers. The pre-trained CNN models are alexnet, googlenet, vgg16, vgg19, densenet201, resnet18, resnet50, resnet101, inceptionv3, inceptionresnetv2, xception, mobilenetv2 and shufflenet. The features and classifiers in machine learning approaches are GLCM, LBP, HOG and KNN, SVM, Naive bay’s respectively. In addition, we also analyse the different paradigms of classifiers. In total, the comparative analysis is carried out in 65 classification models, i.e. 13 in deep feature extraction, 13 in transfer learning and 39 in machine learning approaches. Finally, all the classification models perform better in X-ray image set compare to CT scan image set.

scholarly journals Training AI-Based Feature Extraction Algorithms, for Micro CT Images, Using Synthesized Data

2021 ◽  
Vol 40 (1) ◽  
Author(s):  
Matthew Konnik ◽  
Bahar Ahmadi ◽  
Nicholas May ◽  
Joseph Favata ◽  
Zahra Shahbazi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Learning Algorithms ◽  
Extraction Process ◽  
Machine Learning Algorithms ◽  
Ct Image ◽  
X Ray ◽  
3D Object ◽  
Automated Feature Extraction ◽  
Position And Orientation

AbstractX-ray computed tomography (CT) is a powerful technique for non-destructive volumetric inspection of objects and is widely used for studying internal structures of a large variety of sample types. The raw data obtained through an X-ray CT practice is a gray-scale 3D array of voxels. This data must undergo a geometric feature extraction process before it can be used for interpretation purposes. Such feature extraction process is conventionally done manually, but with the ever-increasing trend of image data sizes and the interest in identifying more miniature features, automated feature extraction methods are sought. Given the fact that conventional computer-vision-based methods, which attempt to segment images into partitions using techniques such as thresholding, are often only useful for aiding the manual feature extraction process, machine-learning based algorithms are becoming popular to develop fully automated feature extraction processes. Nevertheless, the machine-learning algorithms require a huge pool of labeled data for proper training, which is often unavailable. We propose to address this shortage, through a data synthesis procedure. We will do so by fabricating miniature features, with known geometry, position and orientation on thin silicon wafer layers using a femtosecond laser machining system, followed by stacking these layers to construct a 3D object with internal features, and finally obtaining the X-ray CT image of the resulting 3D object. Given that the exact geometry, position and orientation of the fabricated features are known, the X-ray CT image is inherently labeled and is ready to be used for training the machine learning algorithms for automated feature extraction. Through several examples, we will showcase: (1) the capability of synthesizing features of arbitrary geometries and their corresponding labeled images; and (2) use of the synthesized data for training machine-learning based shape classifiers and features parameter extractors.

scholarly journals Computer aid screening of COVID-19 using X-ray and CT scan images: An inner comparison

2021 ◽  
pp. 1-14
Author(s):  
Prabira Kumar Sethy ◽  
Santi Kumari Behera ◽  
Komma Anitha ◽  
Chanki Pandey ◽  
M.R. Khan
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Transfer Learning ◽  
Classification Models ◽  
X Ray ◽  
Computer Aid ◽  
Deep Feature ◽  
Chest X Ray ◽  
Image Set ◽  
Deep Feature Extraction

The objective of this study is to conduct a critical analysis to investigate and compare a group of computer aid screening methods of COVID-19 using chest X-ray images and computed tomography (CT) images. The computer aid screening method includes deep feature extraction, transfer learning, and machine learning image classification approach. The deep feature extraction and transfer learning method considered 13 pre-trained CNN models. The machine learning approach includes three sets of handcrafted features and three classifiers. The pre-trained CNN models include AlexNet, GoogleNet, VGG16, VGG19, Densenet201, Resnet18, Resnet50, Resnet101, Inceptionv3, Inceptionresnetv2, Xception, MobileNetv2 and ShuffleNet. The handcrafted features are GLCM, LBP & HOG, and machine learning based classifiers are KNN, SVM & Naive Bayes. In addition, the different paradigms of classifiers are also analyzed. Overall, the comparative analysis is carried out in 65 classification models, i.e., 13 in deep feature extraction, 13 in transfer learning, and 39 in the machine learning approaches. Finally, all classification models perform better when applying to the chest X-ray image set as comparing to the use of CT scan image set. Among 65 classification models, the VGG19 with SVM achieved the highest accuracy of 99.81%when applying to the chest X-ray images. In conclusion, the findings of this analysis study are beneficial for the researchers who are working towards designing computer aid tools for screening COVID-19 infection diseases.

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

OSTEOPOROSIS AND RISK FACTORS IN THE PATIENTS ON CHRONIC HEMODIALYSIS

2015 ◽  
pp. 50-58
Author(s):  
Thi Dung Nguyen ◽  
Tam Vo
Keyword(s):  
Quality Of Life ◽  
Risk Factors ◽  
Bone Density ◽  
Region Of Interest ◽  
Cross Sectional Study ◽  
Chronic Hemodialysis ◽  
Cross Sectional ◽  
X Ray ◽  
Density Reduction

Background: The patients on hemodialysis have a significantly decreased quality of life. One of many problems which reduce the quality of life and increase the mortality in these patients is osteoporosis and osteoporosis associated fractures. Objectives: To assess the bone density of those on hemodialysis by dual energy X ray absorptiometry and to examine the risk factors of bone density reduction in these patients. Patients and Method: This is a cross-sectional study, including 93 patients on chronic hemodialysis at the department of Hemodialysis at Cho Ray Hospital. Results: Mean bone densities at the region of interest (ROI) neck, trochanter, Ward triangle, intertrochanter and total neck are 0.603 ± 0.105; 0.583 ± 0.121; 0.811 ± 0.166; 0.489 ± 0.146; 0.723 ± 0.138 g/cm2 respectively. The prevalences of osteoporosis at those ROI are 39.8%, 15.1%; 28%; 38.7%; and 26.9% respectively. The prevalences of osteopenia at those ROI are 54.8%; 46.3%; 60.2%; 45.2% and 62.7% respectively. The prevalence of osteopososis in at least one ROI is 52.7% and the prevalence of osteopenia in at least one ROI is 47.3%. There are relations between the bone density at the neck and the gender of the patient and the albuminemia. Bone density at the trochanter is influenced by gender, albuminemia, calcemia and phosphoremia. Bone density at the intertrochanter is affected by the gender. Bone density at the Ward triangle is influenced by age and albuminemia. Total neck bone density is influenced by gender, albuminemia and phosphoremia. Conclusion: Osteoporosis in patients on chronic hemodialysis is an issue that requires our attention. There are many interventionable risk factors of bone density decrease in these patients. Key words: Osteoporosis, DEXA, chronic renal failure, chronic hemodialysis

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