scholarly journals Sliding Window Based Machine Learning System for the Left Ventricle Localization in MR Cardiac Images

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Abdulkader Helwan ◽  
Dilber Uzun Ozsahin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Left Ventricle ◽  
Recognition Rate ◽  
Detection Task ◽  
Learning System ◽  
Backpropagation Neural Network ◽  
Vision Systems ◽  
Test Set ◽  
Cardiac Images

The most commonly encountered problem in vision systems includes its capability to suffice for different scenes containing the object of interest to be detected. Generally, the different backgrounds in which the objects of interest are contained significantly dwindle the performance of vision systems. In this work, we design a sliding windows machine learning system for the recognition and detection of left ventricles in MR cardiac images. We leverage on the capability of artificial neural networks to cope with some of the inevitable scene constraints encountered in medical objects detection tasks. We train a backpropagation neural network on samples of left and nonleft ventricles. We reformulate the left ventricles detection task as a machine learning problem and employ an intelligent system (backpropagation neural network) to achieve the detection task. We treat the left ventricle detection problem as binary classification tasks by assigning collected left ventricle samples as one class, and random (nonleft ventricles) objects are the other class. The trained backpropagation neural network is validated to possess a good generalization power by simulating it with a test set. A recognition rate of 100% and 88% is achieved on the training and test set, respectively. The trained backpropagation neural network is used to determine if the sampled region in a target image contains a left ventricle or not. Lastly, we show the effectiveness of the proposed system by comparing the manual detection of left ventricles drawn by medical experts and the automatic detection by the trained network.

scholarly journals Detection and Classification of Aircraft Fixation Elements during Manufacturing Processes Using a Convolutional Neural Network

2020 ◽  
Vol 10 (19) ◽  
pp. 6856 ◽  
Author(s):  
Leandro Ruiz ◽  
Manuel Torres ◽  
Alejandro Gómez ◽  
Sebastián Díaz ◽  
José M. González ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Technological Development ◽  
Critical Issue ◽  
Machine Learning Techniques ◽  
Manufacturing Processes ◽  
Advanced Manufacturing ◽  
Vision Systems ◽  
Development Capacity

The aerospace sector is one of the main economic drivers that strengthens our present, constitutes our future and is a source of competitiveness and innovation with great technological development capacity. In particular, the objective of manufacturers on assembly lines is to automate the entire process by using digital technologies as part of the transition toward Industry 4.0. In advanced manufacturing processes, artificial vision systems are interesting because their performance influences the liability and productivity of manufacturing processes. Therefore, developing and validating accurate, reliable and flexible vision systems in uncontrolled industrial environments is a critical issue. This research deals with the detection and classification of fasteners in a real, uncontrolled environment for an aeronautical manufacturing process, using machine learning techniques based on convolutional neural networks. Our system achieves 98.3% accuracy in a processing time of 0.8 ms per image. The results reveal that the machine learning paradigm based on a neural network in an industrial environment is capable of accurately and reliably estimating mechanical parameters to improve the performance and flexibility of advanced manufacturing processing of large parts with structural responsibility.

scholarly journals Comparison of rule-based and neural network models for negation detection in radiology reports

2020 ◽  
pp. 1-22 ◽  
Author(s):  
D. Sykes ◽  
A. Grivas ◽  
C. Grover ◽  
R. Tobin ◽  
C. Sudlow ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Language Processing ◽  
Network Models ◽  
Neural Network Models ◽  
Test Set ◽  
Rule Based ◽  
Radiology Reports ◽  
The Neural Network ◽  
Negation Detection

Abstract Using natural language processing, it is possible to extract structured information from raw text in the electronic health record (EHR) at reasonably high accuracy. However, the accurate distinction between negated and non-negated mentions of clinical terms remains a challenge. EHR text includes cases where diseases are stated not to be present or only hypothesised, meaning a disease can be mentioned in a report when it is not being reported as present. This makes tasks such as document classification and summarisation more difficult. We have developed the rule-based EdIE-R-Neg, part of an existing text mining pipeline called EdIE-R (Edinburgh Information Extraction for Radiology reports), developed to process brain imaging reports, (https://www.ltg.ed.ac.uk/software/edie-r/) and two machine learning approaches; one using a bidirectional long short-term memory network and another using a feedforward neural network. These were developed on data from the Edinburgh Stroke Study (ESS) and tested on data from routine reports from NHS Tayside (Tayside). Both datasets consist of written reports from medical scans. These models are compared with two existing rule-based models: pyConText (Harkema et al. 2009. Journal of Biomedical Informatics42(5), 839–851), a python implementation of a generalisation of NegEx, and NegBio (Peng et al. 2017. NegBio: A high-performance tool for negation and uncertainty detection in radiology reports. arXiv e-prints, p. arXiv:1712.05898), which identifies negation scopes through patterns applied to a syntactic representation of the sentence. On both the test set of the dataset from which our models were developed, as well as the largely similar Tayside test set, the neural network models and our custom-built rule-based system outperformed the existing methods. EdIE-R-Neg scored highest on F1 score, particularly on the test set of the Tayside dataset, from which no development data were used in these experiments, showing the power of custom-built rule-based systems for negation detection on datasets of this size. The performance gap of the machine learning models to EdIE-R-Neg on the Tayside test set was reduced through adding development Tayside data into the ESS training set, demonstrating the adaptability of the neural network models.

EXPLORING FEATURES AND CLASSIFIERS TO CLASSIFY GENE EXPRESSION PROFILES OF ACUTE LEUKEMIA

2002 ◽  
Vol 16 (07) ◽  
pp. 831-844 ◽  
Author(s):  
SUNG-BAE CHO
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Expression Profiles ◽  
Lymphoblastic Leukemia ◽  
Recognition Rate ◽  
Correlation Coefficients ◽  
Support Vector ◽  
Self Organizing Map ◽  
Backpropagation Neural Network ◽  
Self Organizing

Bioinformatics has recently drawn a lot of attention to efficiently analyze biological genomic information with information technology, especially pattern recognition. In this paper, we attempt to explore extensive features and classifiers through a comparative study of the most promising feature selection methods and machine learning classifiers. The gene information from a patient's marrow expressed by DNA microarray, which is either the acute myeloid leukemia or acute lymphoblastic leukemia, is used to predict the cancer class. Pearson's and Spearman's correlation coefficients, Euclidean distance, cosine coefficient, information gain, mutual information and signal to noise ratio have been used for feature selection. Backpropagation neural network, self-organizing map, structure adaptive self-organizing map, support vector machine, inductive decision tree and k-nearest neighbor have been used for classification. Experimental results indicate that backpropagation neural network with Pearson's correlation coefficients produces the best result, 97.1% of recognition rate on the test data.

MACHINE LEARNING FOR OBJECT RECOGNITION AND SCENE ANALYSIS

1994 ◽  
Vol 08 (01) ◽  
pp. 259-304 ◽  
Author(s):  
Y. KODRATOFF ◽  
S. MOSCATELLI
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Knowledge Acquisition ◽  
Image Understanding ◽  
Research Field ◽  
Learning System ◽  
Model Matching ◽  
Vision Systems ◽  
Recent Developments ◽  
Optimizing Model

Learning is a critical research field for autonomous computer vision systems. It can bring solutions to the knowledge acquisition bottleneck of image understanding systems. Recent developments of machine learning for computer vision are reported in this paper. We describe several different approaches for learning at different levels of the image understanding process, including learning 2-D shape models, learning strategic knowledge for optimizing model matching, learning for adaptive target recognition systems, knowledge acquisition of constraint rules for labelling and automatic parameter optimization for vision systems. Each approach will be commented on and its strong and weak points will be underlined. In conclusion we will suggest what could be the “ideal” learning system for vision.

scholarly journals Improved Crack Detection and Recognition Based on Convolutional Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Keqin Chen ◽  
Amit Yadav ◽  
Asif Khan ◽  
Yixin Meng ◽  
Kun Zhu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Optimization Algorithm ◽  
Crack Detection ◽  
Recognition Rate ◽  
Machine Learning Methods ◽  
Long Time ◽  
Detection And Recognition

Concrete cracks are very serious and potentially dangerous. There are three obvious limitations existing in the present machine learning methods: low recognition rate, low accuracy, and long time. Improved crack detection based on convolutional neural networks can automatically detect whether an image contains cracks and mark the location of the cracks, which can greatly improve the monitoring efficiency. Experimental results show that the Adam optimization algorithm and batch normalization (BN) algorithm can make the model converge faster and achieve the maximum accuracy of 99.71%.

scholarly journals Fundus Image Classification Using Convolutional Neural Network

2021 ◽  
Author(s):  
U. Savitha ◽  
Kodali Lahari Chandana ◽  
A. Cathrin Sagayam ◽  
S. Bhuvaneswari
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Learning System ◽  
Machine Learning Techniques ◽  
Eye Disease ◽  
Clinical Use ◽  
Fundus Image ◽  
Learning Techniques ◽  
Eye Pattern

Different eye disease has clinical use in defining of the actual status of eye, in the outcome of the medication and other alternatives in the curative phase. Mainly simplicity, clinical nature are the most important requirements for any classification system. In the existing they used different machine learning techniques to detect only single disease. Whereas deep learning system, which is named as Convolutional neural networks (CNNs) can show hierarchical representing of images between disease eye and normal eye pattern.

scholarly journals COMPARATIVE STUDY OF CAPSULE NEURAL NETWORK IN VARIOUS APPLICATIONS

2019 ◽  
Vol 01 (01) ◽  
pp. 19-27 ◽  
Author(s):  
Vijayakumar T
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Comparative Study ◽  
Computer Graphic ◽  
Spatial Location ◽  
Neural System ◽  
Learning System ◽  
The Comparative Study ◽  
Inverse Operation

The advancement in the machine learning and the computer vision has caused several improvements and development in numerous of domains. Capsule neural networks are one such machine learning system that imitates the neural system and develops the structures based on the hierarchical relationships. It does the inverse operation of the computer graphic in representing an object by, segregating the object in the image into different part and viewing the in-existing relationship between the each parts to represent in order to preserve even the minute details related to the object, unlike CNN that losses major of the information’s related to the spatial location of the object that are essential in the segmentation and the detection. So the paper presents the comparative study of the capsule neural network in various application, presenting the efficiency of the capsules networks over the convolutional neural networks.

scholarly journals Predicting eyes at risk for rapid glaucoma progression based on an initial visual field test using machine learning

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249856
Author(s):  
Scott R. Shuldiner ◽  
Michael V. Boland ◽  
Pradeep Y. Ramulu ◽  
C. Gustavo De Moraes ◽  
Tobias Elze ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
At Risk ◽  
Logistic Regression ◽  
Visual Field ◽  
Random Forest ◽  
Rapid Progression ◽  
Test Set ◽  
Glaucoma Progression ◽  
Summary Measures

Objective To assess whether machine learning algorithms (MLA) can predict eyes that will undergo rapid glaucoma progression based on an initial visual field (VF) test. Design Retrospective analysis of longitudinal data. Subjects 175,786 VFs (22,925 initial VFs) from 14,217 patients who completed ≥5 reliable VFs at academic glaucoma centers were included. Methods Summary measures and reliability metrics from the initial VF and age were used to train MLA designed to predict the likelihood of rapid progression. Additionally, the neural network model was trained with point-wise threshold data in addition to summary measures, reliability metrics and age. 80% of eyes were used for a training set and 20% were used as a test set. MLA test set performance was assessed using the area under the receiver operating curve (AUC). Performance of models trained on initial VF data alone was compared to performance of models trained on data from the first two VFs. Main outcome measures Accuracy in predicting future rapid progression defined as MD worsening more than 1 dB/year. Results 1,968 eyes (8.6%) underwent rapid progression. The support vector machine model (AUC 0.72 [95% CI 0.70–0.75]) most accurately predicted rapid progression when trained on initial VF data. Artificial neural network, random forest, logistic regression and naïve Bayes classifiers produced AUC of 0.72, 0.70, 0.69, 0.68 respectively. Models trained on data from the first two VFs performed no better than top models trained on the initial VF alone. Based on the odds ratio (OR) from logistic regression and variable importance plots from the random forest model, older age (OR: 1.41 per 10 year increment [95% CI: 1.34 to 1.08]) and higher pattern standard deviation (OR: 1.31 per 5-dB increment [95% CI: 1.18 to 1.46]) were the variables in the initial VF most strongly associated with rapid progression. Conclusions MLA can be used to predict eyes at risk for rapid progression with modest accuracy based on an initial VF test. Incorporating additional clinical data to the current model may offer opportunities to predict patients most likely to rapidly progress with even greater accuracy.

scholarly journals Wine Quality Prediction Using Machine Learning

2021 ◽  
Vol 9 (8) ◽  
pp. 1997-2001
Author(s):  
Vrusha P. Sangodkar
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Feature Selection ◽  
Bp Neural Network ◽  
Data Extraction ◽  
Backpropagation Neural Network ◽  
Wine Quality ◽  
Machine Learning Methods ◽  
Culture Consumption

Abstract: Nowadays people are living a luxurious lifestyle, wine has become a part of one's culture. consumption of wine is very common throughout the world so its quality is very important. hence its important to analyse wine quality quality of the wines are usually checked by humans through tasting but it has other physicochemical attributes which affects the taste but the process is slow hence machine learning methods can be used for the same. dataset is taken and feature selection is done using pca feature selection and then accuracy is find using SVM, backpropagation neural network and Random forest algorithm to find which model fits best and gives greater accuracy. Keywords: Data Extraction, PCA, SVM,BP neural network, Randomforest

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