scholarly journals Effect of GAN-based image standardization on MR knee bone-tissue classification performance

10.29007/d18s ◽  
2020 ◽  
Author(s):  
Vincent Jaouen ◽  
Guillaume Dardenne ◽  
Florent Tixier ◽  
Éric Stindel ◽  
Dimitris Visvikis
Keyword(s):  
Machine Learning ◽  
Medical Imaging ◽  
Bone Tissue ◽  
Pulse Sequences ◽  
Image Synthesis ◽  
Magnetic Resonance Images ◽  
Machine Learning Algorithms ◽  
Pool Data ◽  
Tissue Classification ◽  
High Definition

Due to their sensitivity to acquisition parameters, medical images such as magnetic resonance images (MRI), Positron Emission tomography (PET) or Computed Tomography (CT) images often suffer from a kind of variability unrelated to diagnostic power, often known as the center effect (CE). This is especially true in MRI, where units are arbitrary and image values can strongly depend on subtle variations in the pulse sequences [1]. Due to the CE it is particularly difficult in various medical imaging applications to 1) pool data coming from several centers or 2) train machine learning algorithms requiring large homogeneous training sets. There is therefore a clear need for image standardization techniques aiming at reducing this effect.Considerable improvements in image synthesis have been achieved over the recent years using (deep) machine learning. Models based on generative adversarial neural networks (GANs) now enable the generation of high definition images capable of fooling the human eye [2]. These methods are being increasingly used in medical imaging for various cross-modality (image-to-image) applications such as MR to CT synthesis [3]. However, they have been seldom used for the purpose of image standardization, i.e. for reducing the CE [4].In this work, we explore the potential advantage of embedding a standardization step using GANs prior to knee bone tissue classification in MRI. We consider image standardization as a within-domain image synthesis problem, where our objective is to learn a mapping between a domain D constituted of heterogeneous images and a reference domain R showing one or several images of desired image characteristics.Preliminary results suggest a beneficial impact of such a standardization step on segmentation performance.

scholarly journals Estimating PQoS of Video Conferencing on Wi-Fi Networks Using Machine Learning

2021 ◽  
Vol 13 (3) ◽  
pp. 63
Author(s):  
Maghsoud Morshedi ◽  
Josef Noll
Keyword(s):  
Machine Learning ◽  
Network Performance ◽  
Service Providers ◽  
Video Conferencing ◽  
Machine Learning Algorithms ◽  
Performance Parameters ◽  
High Definition ◽  
Internet Service ◽  
Novel Approach ◽  
5 Ghz

Video conferencing services based on web real-time communication (WebRTC) protocol are growing in popularity among Internet users as multi-platform solutions enabling interactive communication from anywhere, especially during this pandemic era. Meanwhile, Internet service providers (ISPs) have deployed fiber links and customer premises equipment that operate according to recent 802.11ac/ax standards and promise users the ability to establish uninterrupted video conferencing calls with ultra-high-definition video and audio quality. However, the best-effort nature of 802.11 networks and the high variability of wireless medium conditions hinder users experiencing uninterrupted high-quality video conferencing. This paper presents a novel approach to estimate the perceived quality of service (PQoS) of video conferencing using only 802.11-specific network performance parameters collected from Wi-Fi access points (APs) on customer premises. This study produced datasets comprising 802.11-specific network performance parameters collected from off-the-shelf Wi-Fi APs operating at 802.11g/n/ac/ax standards on both 2.4 and 5 GHz frequency bands to train machine learning algorithms. In this way, we achieved classification accuracies of 92–98% in estimating the level of PQoS of video conferencing services on various Wi-Fi networks. To efficiently troubleshoot wireless issues, we further analyzed the machine learning model to correlate features in the model with the root cause of quality degradation. Thus, ISPs can utilize the approach presented in this study to provide predictable and measurable wireless quality by implementing a non-intrusive quality monitoring approach in the form of edge computing that preserves customers’ privacy while reducing the operational costs of monitoring and data analytics.

scholarly journals Impact of Deep Learning in Medical Imaging: a Systematic New Proposed Model

2019 ◽  
Vol 8 (3S2) ◽  
pp. 112-118
Keyword(s):  
Deep Learning ◽  
Medical Imaging ◽  
Learning Algorithms ◽  
Brain Lesion ◽  
Research Area ◽  
Magnetic Resonance Images ◽  
Machine Learning Algorithms ◽  
Proposed Model ◽  
Active Research ◽  
Active Research Area

For years’ radiologist and clinician continues to employs various approaches, machine learning algorithms included to detect, diagnose, and prevent diseases using medical imaging. Recent advances in deep learning made medical imaging analysis and processing an active research area, various algorithms for segmentation, detection, and classification have been proposed. In this survey, we describe the trends of deep learning algorithms use in medical imaging, their architecture, hardware, and software used are all discussed. We concluded with the proposed model for brain lesion segmentation and classification using Magnetic Resonance Images (MRI).

scholarly journals Identification of potentially painful disc fissures in magnetic resonance images using machine-learning modelling

2021 ◽  
Author(s):  
Lagerstrand Kerstin ◽  
Hebelka Hanna ◽  
Brisby Helerna
Keyword(s):  
Machine Learning ◽  
Magnetic Resonance ◽  
Diagnostic Accuracy ◽  
Negative Control ◽  
Magnetic Resonance Images ◽  
Machine Learning Algorithms ◽  
Pain Model ◽  
Accuracy And Precision ◽  
Magnetic Resonance Imaging Mri ◽  
Very High

Abstract Purpose It is suggested that non-specific low back pain (LBP) can be related to nerve ingrowth along granulation tissue in disc fissures, extending into the outer layers of the annulus fibrosus. Present study aimed to investigate if machine-learning modelling of magnetic resonance imaging (MRI) data can classify such fissures as well as pain, provoked by discography, with plausible accuracy and precision. Methods The study was based on previously collected data from 30 LBP patients (age = 26–64 years, 11 males). Pressure-controlled discography was performed in 86 discs with pain-positive discograms, categorized as concordant pain-response at a pressure ≤ 50 psi and for each patient one negative control disc. The CT-discograms were used for categorization of fissures. MRI values and standard deviations were extracted from the midsagittal part and from 5 different sub-regions of the discs. Machine-learning algorithms were trained on the extracted MRI markers to classify discs with fissures extending into the outer annulus or not, as well as to classify discs as painful or non-painful. Results Discs with outer annular fissures were classified in MRI with very high precision (mean of 10 repeated testings: 99%) and accuracy (mean: 97%) using machine-learning modelling, but the pain model only demonstrated moderate diagnostic accuracy (mean accuracy: 69%; precision: 71%). Conclusion The present study showed that machine-learning modelling based on MRI can classify outer annular fissures with very high diagnostic accuracy and, hence, enable individualized diagnostics. However, the model only demonstrated moderate diagnostic accuracy regarding pain that could be assigned to either a non-sufficient model or the used pain reference.

scholarly journals Analysis of machine learning algorithms in brain tumour prediction

2021 ◽  
Vol 2070 (1) ◽  
pp. 012090
Author(s):  
M Gupta ◽  
S K Sharma ◽  
R Saxena ◽  
S Arora
Keyword(s):  
Machine Learning ◽  
Brain Tumour ◽  
Learning Algorithms ◽  
Magnetic Resonance Images ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Primary Concern ◽  
Malignant Brain Tumour ◽  
Structural Consistency ◽  
Cancerous Cells

Abstract The tumour is fundamentally an excessive development of dangerous cells in any part of the body, while a tumour in a brain is an unreasonable development of cancerous cells in the brain. Brain tumour can be either benign or malignant. The benign brain tumour has structural consistency and does not include active (cancer) cells, but the malignant brain tumour has no structure consistency and includes active cells. The primary concern is to segment, detect, and extract the infected tumour area from magnetic resonance images (MRI) which are being performed by radiologists or medical experts, and their accuracy is totally dependent on their experience only. Thus, it becomes very essential to overcome these limitations by the use of artificial intelligence. The current paper uses various machine learning algorithms as well as their features to design a structure to predict brain tumour at an early phase by using different classifiers and comparing their respective accuracy parameters.

scholarly journals Segmentation of Brain Tumors from MRI Images Using Convolutional Autoencoder

2021 ◽  
Vol 11 (9) ◽  
pp. 4317
Author(s):  
Milica M. Badža ◽  
Marko Č. Barjaktarović
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Medical Diagnostics ◽  
Magnetic Resonance Images ◽  
Clinical Diagnostics ◽  
Machine Learning Algorithms ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Generalization Ability ◽  
Data Division

The use of machine learning algorithms and modern technologies for automatic segmentation of brain tissue increases in everyday clinical diagnostics. One of the most commonly used machine learning algorithms for image processing is convolutional neural networks. We present a new convolutional neural autoencoder for brain tumor segmentation based on semantic segmentation. The developed architecture is small, and it is tested on the largest online image database. The dataset consists of 3064 T1-weighted contrast-enhanced magnetic resonance images. The proposed architecture’s performance is tested using a combination of two different data division methods, and two different evaluation methods, and by training the network with the original and augmented dataset. Using one of these data division methods, the network’s generalization ability in medical diagnostics was also tested. The best results were obtained for record-wise data division, training the network with the augmented dataset. The average accuracy classification of pixels is 99.23% and 99.28% for 5-fold cross-validation and one test, respectively, and the average dice coefficient is 71.68% and 72.87%. Considering the achieved performance results, execution speed, and subject generalization ability, the developed network has great potential for being a decision support system in everyday clinical practice.

scholarly journals Convolutional neural networks on eye tracking trajectories in patients with spatial neglect

2020 ◽  
Author(s):  
Benedetta Franceschiello ◽  
Alexia Bourgeois ◽  
Astrid Minier ◽  
Micah M. Murray ◽  
Pierre Pouget ◽  
...  
Keyword(s):  
Machine Learning ◽  
Diffusion Tensor ◽  
Magnetic Resonance Images ◽  
Machine Learning Algorithms ◽  
Spatial Neglect ◽  
Behavioral Data ◽  
Machine Learning Techniques ◽  
Quantitative Prediction ◽  
Visuospatial Neglect ◽  
Movement Trajectories

Eye-movement trajectories are rich behavioral data, providing a window onto how the brain processes information. Analyses of these trajectories can be automated and benefit from machine learning algorithms. Among those, deep learning has recently proven very successful, setting new state-of-art results in many computer vision applications, including medical diagnosis systems. In this paper, we address the challenge of diagnosing and quantifying signs of visuospatial neglect from saccadic eye trajectories recorded in healthy controls and in brain-damaged patients with spatial neglect. We show how machine learning techniques, such as deep networks, can predict the patient's status with unprecedented accuracy, benchmarking the algorithm prediction with structural Magnetic Resonance Images (MRI) of the patients' brain lesions and their Diffusion Tensor Imaging (DTI) tracts. Preliminary evidence of correlation between MRI data and the algorithm scores suggest that a quantitative prediction of the patients' impairment based only onto the behavioral data of eye trajectories seem possible, therefore opening to new horizons in the field of non-invasive diagnostics.

scholarly journals BCM-VEMT: Classification of Brain Cancer from MRI Images using Deep Learning and Ensemble of Machine Learning Techniques

2021 ◽  
Author(s):  
Prottoy Saha ◽  
Rudra Das ◽  
Shanta Kumar Das
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Brain Cancer ◽  
Weighted Average ◽  
Magnetic Resonance Images ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Cancer Type ◽  
Cancer Types ◽  
Tumor Types

Abstract Brain Cancer is quite possibly the most driving reason for death in recent years. Appropriate diagnosis of the cancer type empowers the specialists to make the right choice of treatment, decision and to save the patient's life. It goes no saying the importance of a computer-aided diagnosis system with image processing that can classify the tumor types correctly. In this paper, an enhanced approach has been proposed, that can classify brain tumor types from Magnetic Resonance Images (MRI) using deep learning and an ensemble of Machine Learning Algorithms. The system named BCM-VEMT can classify among four different classes that consist of three categories of Brain Cancers (Glioma, Meningioma, and Pituitary) and Non-Cancerous which means Normal type. A Convolutional Neural Network is developed to extract deep features from the MRI images. Then these extracted deep features are fed into multi-class ML classifiers to classify among these cancer types. Finally, a weighted average ensemble of classifiers is used to achieve better performance by combining the results of each ML classifier. The dataset of the system has a total of 3787 MRI images of four classes. BCM-VEMT has achieved better performance with 97.90% accuracy for the Glioma class, 98.94% accuracy for the Meningioma class, 98.00% accuracy for the Normal class, 98.92% accuracy for the Pituitary class, and overall accuracy of 98.42%. BCM-VEMT can have a great significance in classifying Brain Cancer types.

scholarly journals Ensemble Machine Learning Algorithms for Prediction and Classification of Medical Images

2021 ◽  
Author(s):  
Racheal S. Akinbo ◽  
Oladunni A. Daramola
Keyword(s):  
Machine Learning ◽  
Medical Imaging ◽  
Ensemble Learning ◽  
Medical Images ◽  
Learning Algorithms ◽  
Theoretical Background ◽  
Machine Learning Algorithms ◽  
Ensemble Machine Learning ◽  
Machine Learning Applications

The employment of machine learning algorithms in disease classification has evolved as a precision medicine for scientific innovation. The geometric growth in various machine learning systems has paved the way for more research in the medical imaging process. This research aims to promote the development of machine learning algorithms for the classification of medical images. Automated classification of medical images is a fascinating application of machine learning and they have the possibility of higher predictability and accuracy. The technological advancement in the processing of medical imaging will help to reduce the complexities of diseases and some existing constraints will be greatly minimized. This research exposes the main ensemble learning techniques as it covers the theoretical background of machine learning, applications, comparison of machine learning and deep learning, ensemble learning with reviews of state-of the art literature, framework, and analysis. The work extends to medical image types, applications, benefits, and operations. We proposed the application of the ensemble machine learning approach in the classification of medical images for better performance and accuracy. The integration of advanced technology in clinical imaging will help in the prompt classification, prediction, early detection, and a better interpretation of medical images, this will, in turn, improves the quality of life and expands the clinical bearing for machine learning applications.

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