scholarly journals CT Texture Analysis of Abdominal Lesions – Part I.: Liver lesions

Imaging ◽  
2021 ◽  
Author(s):  
Bettina Katalin Budai ◽  
Veronica Frank ◽  
Sonaz Shariati ◽  
Bence Fejér ◽  
Ambrus Tóth ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Liver Tumors ◽  
Machine Learning Techniques ◽  
Focal Liver Lesions ◽  
Liver Lesions ◽  
Non Invasive ◽  
Learning Techniques ◽  
Ct Texture Analysis ◽  
Malignant Liver ◽  
Abdominal Lesions

ABSTRACTArtificial Intelligence and the use of radiomics analysis have been of great interest in the last decade in the field of imaging. CT texture analysis (CTTA) is a new and emerging field in radiomics, which seems promising in the assessment and diagnosis of both focal and diffuse liver lesions. The utilization of CTTA has only been receiving great attention recently, especially for response evaluation and prognostication of different oncological diagnoses. Radiomics, combined with machine learning techniques, offers a promising opportunity to accurately detect or differentiate between focal liver lesions based on their unique texture parameters. In this review article, we discuss the unique ability of radiomics in the diagnostics and prognostication of both focal and diffuse liver lesions. We also provide a brief review of radiogenomics and summarize its potential role of in the non-invasive diagnosis of malignant liver tumors.

Non-invasive Deception Detection in Videos Using Machine Learning Techniques

2021 ◽  
Author(s):  
Siam Islam ◽  
Popin Saha ◽  
Touhidul Chowdhury ◽  
Asif Sorowar ◽  
Raqeebir Rab
Keyword(s):  
Machine Learning ◽  
Deception Detection ◽  
Machine Learning Techniques ◽  
Non Invasive ◽  
Learning Techniques

Soft Computing-Based Early Detection of Parkinson's Disease Using Non-Invasive Method Based on Speech Analysis

2021 ◽  
pp. 329-340
Author(s):  
Chandrasekar Ravi
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Speech Analysis ◽  
Machine Learning Techniques ◽  
Speech Signals ◽  
Fuzzy Classifier ◽  
Non Invasive ◽  
Specificity And Sensitivity ◽  
Learning Techniques ◽  
Invasive Method

This chapter aims to use the speech signals that are a behavioral bio-marker for Parkinson's disease. The victim's vocabulary is mostly lost, or big gaps are observed when they are talking or the conversation is abruptly stopped. Therefore, speech analysis could help to identify the complications in conversation from the inception of the symptoms of Parkinson's disease in initial phases itself. Speech can be regularly logged in an unobstructed approach and machine learning techniques can be applied and analyzed. Fuzzy logic-based classifier is proposed for learning from the training speech signals and classifying the test speech signals. Brainstorm optimization algorithm is proposed for extracting the fuzzy rules from the speech data, which is used by fuzzy classifier for learning and classification. The performance of the proposed classifier is evaluated using metrics like accuracy, specificity, and sensitivity, and compared with benchmark classifiers like SVM, naïve Bayes, k-means, and decision tree. It is observed that the proposed classifier outperforms the benchmark classifiers.

scholarly journals Non-Intrusive Load Monitoring of Residential Water-Heating Circuit Using Ensemble Machine Learning Techniques

Inventions ◽  
2020 ◽  
Vol 5 (4) ◽  
pp. 57
Author(s):  
Attique Ur Rehman ◽  
Tek Tjing Lie ◽  
Brice Vallès ◽  
Shafiqur Rahman Tito
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Machine Learning Techniques ◽  
Learning Models ◽  
Water Heating ◽  
Energy Monitoring ◽  
Non Invasive ◽  
Ensemble Machine Learning ◽  
Learning Techniques ◽  
Load Monitoring

The recent advancement in computational capabilities and deployment of smart meters have caused non-intrusive load monitoring to revive itself as one of the promising techniques of energy monitoring. Toward effective energy monitoring, this paper presents a non-invasive load inference approach assisted by feature selection and ensemble machine learning techniques. For evaluation and validation purposes of the proposed approach, one of the major residential load elements having solid potential toward energy efficiency applications, i.e., water heating, is considered. Moreover, to realize the real-life deployment, digital simulations are carried out on low-sampling real-world load measurements: New Zealand GREEN Grid Database. For said purposes, MATLAB and Python (Scikit-Learn) are used as simulation tools. The employed learning models, i.e., standalone and ensemble, are trained on a single household’s load data and later tested rigorously on a set of diverse households’ load data, to validate the generalization capability of the employed models. This paper presents a comprehensive performance evaluation of the presented approach in the context of event detection, feature selection, and learning models. Based on the presented study and corresponding analysis of the results, it is concluded that the proposed approach generalizes well to the unseen testing data and yields promising results in terms of non-invasive load inference.

scholarly journals Assessment of Non-Invasive Blood Pressure Prediction from PPG and rPPG Signals Using Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6022
Author(s):  
Fabian Schrumpf ◽  
Patrick Frenzel ◽  
Christoph Aust ◽  
Georg Osterhoff ◽  
Mirco Fuchs
Keyword(s):  
Blood Pressure ◽  
Prediction Error ◽  
Machine Learning Techniques ◽  
Systematic Evaluation ◽  
Average Error ◽  
Prediction Errors ◽  
Invasive Blood Pressure ◽  
Specific Data ◽  
Non Invasive ◽  
Learning Techniques

Exploiting photoplethysmography signals (PPG) for non-invasive blood pressure (BP) measurement is interesting for various reasons. First, PPG can easily be measured using fingerclip sensors. Second, camera based approaches allow to derive remote PPG (rPPG) signals similar to PPG and therefore provide the opportunity for non-invasive measurements of BP. Various methods relying on machine learning techniques have recently been published. Performances are often reported as the mean average error (MAE) on the data which is problematic. This work aims to analyze the PPG- and rPPG based BP prediction error with respect to the underlying data distribution. First, we train established neural network (NN) architectures and derive an appropriate parameterization of input segments drawn from continuous PPG signals. Second, we use this parameterization to train NNs with a larger PPG dataset and carry out a systematic evaluation of the predicted blood pressure. The analysis revealed a strong systematic increase of the prediction error towards less frequent BP values across NN architectures. Moreover, we tested different train/test set split configurations which underpin the importance of a careful subject-aware dataset assignment to prevent overly optimistic results. Third, we use transfer learning to train the NNs for rPPG based BP prediction. The resulting performances are similar to the PPG-only case. Finally, we apply different personalization techniques and retrain our NNs with subject-specific data for both the PPG-only and rPPG case. Whilst the particular technique is less important, personalization reduces the prediction errors significantly.

scholarly journals Can Autism Be Diagnosed with Artificial Intelligence? A Narrative Review

Diagnostics ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2032
Author(s):  
Ahmad Chaddad ◽  
Jiali Li ◽  
Qizong Lu ◽  
Yujie Li ◽  
Idowu Paul Okuwobi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Texture Analysis ◽  
Molecular Data ◽  
Autism Spectrum ◽  
Invasive Technique ◽  
Non Invasive ◽  
Learning Techniques ◽  
Healthy Control ◽  
Aided Diagnosis

Radiomics with deep learning models have become popular in computer-aided diagnosis and have outperformed human experts on many clinical tasks. Specifically, radiomic models based on artificial intelligence (AI) are using medical data (i.e., images, molecular data, clinical variables, etc.) for predicting clinical tasks such as autism spectrum disorder (ASD). In this review, we summarized and discussed the radiomic techniques used for ASD analysis. Currently, the limited radiomic work of ASD is related to the variation of morphological features of brain thickness that is different from texture analysis. These techniques are based on imaging shape features that can be used with predictive models for predicting ASD. This review explores the progress of ASD-based radiomics with a brief description of ASD and the current non-invasive technique used to classify between ASD and healthy control (HC) subjects. With AI, new radiomic models using the deep learning techniques will be also described. To consider the texture analysis with deep CNNs, more investigations are suggested to be integrated with additional validation steps on various MRI sites.

Quantifying Emotional States Based on Body Language Data Using Non Invasive Sensors

2014 ◽  
Author(s):  
Ishan Behoora ◽  
Conrad S. Tucker
Keyword(s):  
Real Time ◽  
Body Language ◽  
Machine Learning Techniques ◽  
Emotional States ◽  
Participant Engagement ◽  
Non Invasive ◽  
Learning Techniques ◽  
Language Data ◽  
Position Data ◽  
Human Participants

Determining participant engagement is an important issue across a large number of fields, ranging from entertainment to education. Traditionally, feedback from participants is taken after the activity has been completed. Alternately, continuous observation by trained humans is needed. Thus, there is a need for an automated real time solution. In this paper, the authors propose a data mining driven approach that models a participant’s engagement, based on body language data acquired in real time using non-invasive sensors. Skeletal position data, that approximates human body motions, is acquired from participants using off the shelf, non-invasive sensors. Thereafter, machine learning techniques are employed to detect body language patterns representing emotions such as delight, interest, boredom, frustration, and confusion. The methodology proposed in this paper enables researchers to predict the participants’ engagement levels in real time with high accuracy above 98%. A case study involving human participants enacting eight body language poses, is used to illustrate the effectiveness of the methodology. Finally, this methodology highlights the potential of a real time, automated engagement detection using non-invasive sensors which can ultimately have applications in a large variety of areas such as lectures, gaming and classroom learning.

Sign in / Sign up

Export Citation Format

Share Document