scholarly journals Assessing Automated Machine Learning Service to Detect COVID-19 from X-Ray and CT Images: A Real-Time Smartphone Application Case Study

2020 ◽  
Author(s):  
Mohammad Razib Mustafiz ◽  
Khaled Mohsin
Keyword(s):  
Machine Learning ◽  
Lung Disease ◽  
Real Time ◽  
Web Application ◽  
Medical Image ◽  
Ct Images ◽  
X Ray ◽  
Medical Image Diagnosis ◽  
The World ◽  
Trained Network

AI is leveraging all aspects of life. Medical services are not untouched. Especially in the field of medical image processing and diagnosis. Big IT and Biotechnology companies are investing millions of dollars in medical and AI research. The recent outbreak of SARS COV-2 gave us a unique opportunity to study for a non interventional and sustainable AI solution. Lung disease remains a major healthcare challenge with high morbidity and mortality worldwide. The predominant lung disease was lung cancer. Until recently, the world has witnessed the global pandemic of COVID19, the Novel coronavirus outbreak. We have experienced how viral infection of lung and heart claimed thousands of lives worldwide. With the unprecedented advancement of Artificial Intelligence in recent years, Machine learning can be used to easily detect and classify medical imagery. It is much faster and most of the time more accurate than human radiologists. Once implemented, it is more cost-effective and time-saving. In our study, we evaluated the efficacy of Microsoft Cognitive Service to detect and classify COVID19 induced pneumonia from other Viral/Bacterial pneumonia based on X-Ray and CT images. We wanted to assess the implication and accuracy of the Automated ML-based Rapid Application Development (RAD) environment in the field of Medical Image diagnosis. This study will better equip us to respond with an ML-based diagnostic Decision Support System(DSS) for a Pandemic situation like COVID19. After optimization, the trained network achieved 96.8% Average Precision which was implemented as a Web Application for consumption. However, the same trained network did not perform like Web Application when ported to Smartphone for Real-time inference, which was our main interest of study. The authors believe, there is scope for further study on this issue. One of the main goals of this study was to develop and evaluate the performance of AI-powered Smartphone-based Real-time Applications. Facilitating primary diagnostic services in less equipped and understaffed rural healthcare centers of the world with unreliable internet service.

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.

Web-Based Machine Learning Application for Heart Disease Prediction

2022 ◽  
pp. 383-393
Author(s):  
Lokesh M. Giripunje ◽  
Tejas Prashant Sonar ◽  
Rohit Shivaji Mali ◽  
Jayant C. Modhave ◽  
Mahesh B. Gaikwad
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Random Forest ◽  
Web Application ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Web Based ◽  
The World ◽  
Comprehensive Survey ◽  
Health Organization

Risk because of heart disease is increasing throughout the world. According to the World Health Organization report, the number of deaths because of heart disease is drastically increasing as compared to other diseases. Multiple factors are responsible for causing heart-related issues. Many approaches were suggested for prediction of heart disease, but none of them were satisfactory in clinical terms. Heart disease therapies and operations available are so costly, and following treatment, heart disease is also costly. This chapter provides a comprehensive survey of existing machine learning algorithms and presents comparison in terms of accuracy, and the authors have found that the random forest classifier is the most accurate model; hence, they are using random forest for further processes. Deployment of machine learning model using web application was done with the help of flask, HTML, GitHub, and Heroku servers. Webpages take input attributes from the users and gives the output regarding the patient heart condition with accuracy of having coronary heart disease in the next 10 years.

scholarly journals Data-Driven Intelligent Application for Youtube Video Popularity Analysis using Machine Learning and Statistics

2021 ◽  
Author(s):  
Wenxi Gao ◽  
Ishmael Rico ◽  
Yu Sun
Keyword(s):  
Machine Learning ◽  
Data Processing ◽  
Web Application ◽  
Data Driven ◽  
Accuracy Evaluation ◽  
Video Content ◽  
The Public ◽  
Left Behind ◽  
The World ◽  
Web App

People now prefer to follow trends. Since the time is moving, people can only keep themselves from being left behind if they keep up with the pace of time. There are a lot of websites for people to explore the world, but websites for those who show the public something new are uncommon. This paper proposes an web application to help YouTuber with recommending trending video content because they sometimes have trouble in thinking of the video topic. Our method to solve the problem is basically in four steps: YouTube scraping, data processing, prediction by SVM and the webpage. Users input their thoughts on our web app and computer will scrap the trending page of YouTube and process the data to do prediction. We did some experiments by using different data, and got the accuracy evaluation of our method. The results show that our method is feasible so people can use it to get their own recommendation.

scholarly journals Gabor Dictionary of Sparse Image Patches Selected in Prior Boundaries for 3D Liver Segmentation in CT Images

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Xuehu Wang ◽  
Zhiling Zhang ◽  
Kunlun Wu ◽  
Xiaoping Yin ◽  
Haifeng Guo
Keyword(s):  
Machine Learning ◽  
Image Segmentation ◽  
Medical Image ◽  
Soft Tissues ◽  
Initial Boundary ◽  
Ct Images ◽  
Medical Image Segmentation ◽  
Liver Segmentation ◽  
Boundary Area ◽  
Gabor Dictionary

The gray contrast between the liver and other soft tissues is low, and the boundary is not obvious. As a result, it is still a challenging task to accurately segment the liver from CT images. In recent years, methods of machine learning have become a research hotspot in the field of medical image segmentation because they can effectively use the “gold standard” personalized features of the liver from different data. However, machine learning usually requires a large number of data samples to train the model and improve the accuracy of medical image segmentation. This paper proposed a method for liver segmentation based on the Gabor dictionary of sparse image blocks with prior boundaries. This method reduced the number of samples by selecting the test sample set within the initial boundary area of the liver. The Gabor feature was extracted and the query dictionary was created, and the sparse coefficient was calculated to obtain the boundary information of the liver. By optimizing the reconstruction error and filling holes, a smooth liver boundary was obtained. The proposed method was tested on the MICCAI 2007 dataset and ISBI2017 dataset, and five measures were used to evaluate the results. The proposed method was compared with methods for liver segmentation proposed in recent years. The experimental results show that this method can improve the accuracy of liver segmentation and effectively repair the discontinuity and local overlap of segmentation results.

scholarly journals A Real-Time Machine Learning Based Public Transport Bus-Passenger Information System

2021 ◽  
Author(s):  
Menzi Skhosana ◽  
Absalom Ezugwu
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Public Transport ◽  
Public Transportation ◽  
Web Application ◽  
Predictive Analytics ◽  
Current Status ◽  
Transport Sector ◽  
The Public ◽  
Cost Efficient

The era of Big Data and the Internet of Things is upon us, and it is time for developing countries to take advantage of and pragmatically apply these ideas to solve real-world problems. Many problems faced daily by the public transportation sector can be resolved or mitigated through the collection of appropriate data and application of predictive analytics. In this body of work, we are primarily focused on problems affecting public transport buses. These include the unavailability of real-time information to commuters about the current status of a given bus or travel route; and the inability of bus operators to efficiently assign available buses to routes for a given day based on expected demand for a particular route. A cloud-based system was developed to address the aforementioned. This system is composed of two subsystems, namely a mobile application for commuters to provide the current location and availability of a given bus and other related information, which can also be used by drivers so that the bus can be tracked in real-time and collect ridership information throughout the day, and a web application that serves as a dashboard for bus operators to gain insights from the collected ridership data. These were integrated with a machine learning model trained on collected ridership data to predict the daily ridership for a given route. Our novel system provides a holistic solution to problems in the public transport sector, as it is highly scalable, cost-efficient and takes full advantage of the currently available technologies in comparison with other previous work in this topic.

scholarly journals Identification of Pneumonia Disease Applying an Intelligent Computational Framework Based on Deep Learning and Machine Learning Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Yar Muhammad ◽  
Mohammad Dahman Alshehri ◽  
Wael Mohammed Alenazy ◽  
Truong Vinh Hoang ◽  
Ryan Alturki
Keyword(s):  
Machine Learning ◽  
Medical Image Analysis ◽  
Machine Learning Techniques ◽  
World Health ◽  
X Ray ◽  
Expert Radiologist ◽  
Learning Techniques ◽  
Effective Manner ◽  
The World ◽  
Chest X Ray

Pneumonia is a very common and fatal disease, which needs to be identified at the initial stages in order to prevent a patient having this disease from more damage and help him/her in saving his/her life. Various techniques are used for the diagnosis of pneumonia including chest X-ray, CT scan, blood culture, sputum culture, fluid sample, bronchoscopy, and pulse oximetry. Medical image analysis plays a vital role in the diagnosis of various diseases like MERS, COVID-19, pneumonia, etc. and is considered to be one of the auspicious research areas. To analyze chest X-ray images accurately, there is a need for an expert radiologist who possesses expertise and experience in the desired domain. According to the World Health Organization (WHO) report, about 2/3 people in the world still do not have access to the radiologist, in order to diagnose their disease. This study proposes a DL framework to diagnose pneumonia disease in an efficient and effective manner. Various Deep Convolutional Neural Network (DCNN) transfer learning techniques such as AlexNet, SqueezeNet, VGG16, VGG19, and Inception-V3 are utilized for extracting useful features from the chest X-ray images. In this study, several machine learning (ML) classifiers are utilized. The proposed system has been trained and tested on chest X-ray and CT images dataset. In order to examine the stability and effectiveness of the proposed system, different performance measures have been utilized. The proposed system is intended to be beneficial and supportive for medical doctors to accurately and efficiently diagnose pneumonia disease.

scholarly journals Real Time-Detection of Lung Cancer

2020 ◽  
Vol 9 (1) ◽  
pp. 242-249
Keyword(s):  
Lung Cancer ◽  
Wavelet Transform ◽  
Real Time ◽  
Computer Tomography ◽  
Ct Images ◽  
Order Method ◽  
Complex Wavelet Transform ◽  
The World ◽  
Lung Infections ◽  
Component Extraction

Lung infections are the most intense ailments that influence the lungs. Lung assumes a fundamental job which takes care in the breathing procedure in people. Lung ailments is said to be the most widely recognized ailments around the world, particularly in India it is increasingly normal. The regular maladies, for example, pleural emanation and typical lung can be recognized and grouped right now. This paper introduces a PC helped order Method in Computer Tomography (CT) Images of lungs created utilizing NN. The significant reason for this framework is to recognize and characterize the most widely recognized lung ailments that causes the significant issues by viable component extraction through Dual-Tree Complex Wavelet Transform and GLCM Features.Right now whole lung is fragmented from the CT Images and the parameters are determined from the divided picture. The parameters are determined utilizing GLCM. We Propose and assess the Network intended for grouping of ILD designs. The parameters gives the greatest grouping Accuracy. After outcome we propose the bunching to portion the injury part from irregular lung.

scholarly journals PREDICTIVE MACHINE LEARNING (ML) ALGORITHM USING IOT FRAMEWORK FOR NOVEL CORONA VIRUS (COVID-19)

2021 ◽  
Vol 57 (9) ◽  
pp. 6328-6336
Author(s):  
G. S. N. Murthy, M. V. Sangameswar, Venubabu Rachapudi, Mylavarapu Kalyan Ram
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Research Work ◽  
Healthcare Industry ◽  
Blood Oxygen ◽  
Nearest Neighbour ◽  
Blood Oxygen Level ◽  
Clinical Methods ◽  
The World ◽  
The Internet Of Things

During earlier months of the pandemic COVID-19 with no recommended cure or vaccine available only solution to destroy the chain is self-isolation which can be maintained by physical distancing. This is now understood that the world require much faster solution to accommodate and deal with the future COVID-19 spread over the world by non-clinical methods namely data mining, augmented intelligence and several Artificial Intelligence (AI) techniques. It has become a huge hindrance to mitigate for the healthcare industry to provide more potential involved for patient's diagnosis and also for effective prognosis of 2019-CoV pandemic. Therefore, the proposed framework is implemented with the Internet of Things (IoTs) in healthcare industry for collecting the symptom data of real-time that is beneficial in predicting whether the person gets infected with COVID-19 virus or not. This can be done through various signs namely body temperature, blood oxygen level, headache, coughing patterns, etc. Thus, the research work focused on faster identification of COVID-19 virus infection cases potentially using Machine Learning (ML) algorithm from the real-time symptom data. Moreover, the obtained results have illustrated that K-Nearest Neighbour (KNN) algorithm is highly efficient while compared with other ML algorithms such as Naive Bayes and Logistic Regression (LR) in predicting the possible recovery of the infected patients from pandemic COVID-19 with the accuracy of 96.85%.

scholarly journals Saturation Modeling of Gas Hydrate Using Machine Learning with X-Ray CT Images

Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 5032
Author(s):  
Sungil Kim ◽  
Kyungbook Lee ◽  
Minhui Lee ◽  
Taewoong Ahn ◽  
Jaehyoung Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gas Hydrate ◽  
Ct Images ◽  
Prediction Performance ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Sand Sample ◽  
X Ray ◽  
Physical Conditions ◽  
Water Gas

This study conducts saturation modeling in a gas hydrate (GH) sand sample with X-ray CT images using the following machine learning algorithms: random forest (RF), convolutional neural network (CNN), and support vector machine (SVM). The RF yields the best prediction performance for water, gas, and GH saturation in the samples among the three methods. The CNN and SVM also exhibit sufficient performances under the restricted conditions, but require improvements to their reliability and overall prediction performance. Furthermore, the RF yields the lowest mean square error and highest correlation coefficient between the original and predicted datasets. Although the GH CT images aid in approximately understanding how fluids act in a GH sample, difficulties were encountered in accurately understanding the behavior of GH in a GH sample during the experiments owing to limited physical conditions. Therefore, the proposed saturation modeling method can aid in understanding the behavior of GH in a GH sample in real-time with the use of an appropriate machine learning method. Furthermore, highly accurate descriptions of each saturation, obtained from the proposed method, lead to an accurate resource evaluation and well-guided optimal depressurization for a target GH field production.

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