scholarly journals SEMANTIC IMAGE ANALYSIS USING A SYMBOLIC NEURAL ARCHITECTURE

2010 ◽  
Vol 29 (3) ◽  
pp. 159 ◽  
Author(s):  
Ilianna Kollia ◽  
Nikolaos Simou ◽  
Andreas Stafylopatis ◽  
Stefanos Kollias
Keyword(s):  
Image Analysis ◽  
Knowledge Base ◽  
Description Logics ◽  
A Priori ◽  
Machine Learning Techniques ◽  
Video Frames ◽  
Multimedia Search ◽  
Neural Network Classifiers ◽  
Classification Of Images ◽  
Semantic Image Analysis

Image segmentation and classification are basic operations in image analysis and multimedia search which have gained great attention over the last few years due to the large increase of digital multimedia content. A recent trend in image analysis aims at incorporating symbolic knowledge representation systems and machine learning techniques. In this paper, we examine interweaving of neural network classifiers and fuzzy description logics for the adaptation of a knowledge base for semantic image analysis. The proposed approach includes a formal knowledge component, which, assisted by a reasoning engine, generates the a-priori knowledge for the image analysis problem. This knowledge is transferred to a kernel based connectionist system, which is then adapted to a specific application field through extraction and use of MPEG-7 image descriptors. Adaptation of the knowledge base can be achieved next. Combined segmentation and classification of images, or video frames, of summer holidays, is the field used to illustrate the good performance of the proposed approach.

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

scholarly journals The Constantly Evolving Role of Medical Image Processing in Oncology: From Traditional Medical Image Processing to Imaging Biomarkers and Radiomics

2021 ◽  
Vol 7 (8) ◽  
pp. 124
Author(s):  
Kostas Marias
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Medical Image ◽  
Biomarker Discovery ◽  
Medical Image Processing ◽  
Machine Learning Techniques ◽  
Imaging Biomarker ◽  
Imaging Biomarkers ◽  
Precision Oncology

The role of medical image computing in oncology is growing stronger, not least due to the unprecedented advancement of computational AI techniques, providing a technological bridge between radiology and oncology, which could significantly accelerate the advancement of precision medicine throughout the cancer care continuum. Medical image processing has been an active field of research for more than three decades, focusing initially on traditional image analysis tasks such as registration segmentation, fusion, and contrast optimization. However, with the advancement of model-based medical image processing, the field of imaging biomarker discovery has focused on transforming functional imaging data into meaningful biomarkers that are able to provide insight into a tumor’s pathophysiology. More recently, the advancement of high-performance computing, in conjunction with the availability of large medical imaging datasets, has enabled the deployment of sophisticated machine learning techniques in the context of radiomics and deep learning modeling. This paper reviews and discusses the evolving role of image analysis and processing through the lens of the abovementioned developments, which hold promise for accelerating precision oncology, in the sense of improved diagnosis, prognosis, and treatment planning of cancer.

Exploring multi-modalities in weather prediction using a univariate graph based on machine learning techniques

2021 ◽  
Author(s):  
Natacha Galmiche ◽  
Nello Blaser ◽  
Morten Brun ◽  
Helwig Hauser ◽  
Thomas Spengler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Standard Deviation ◽  
Probability Distributions ◽  
Weather Prediction ◽  
A Priori ◽  
Clustering Algorithms ◽  
Quantitative Information ◽  
Machine Learning Techniques ◽  
Topological Data Analysis ◽  
Learning Techniques

<p>Probability distributions based on ensemble forecasts are commonly used to assess uncertainty in weather prediction. However, interpreting these distributions is not trivial, especially in the case of multimodality with distinct likely outcomes. The conventional summary employs mean and standard deviation across ensemble members, which works well for unimodal, Gaussian-like distributions. In the case of multimodality this misleads, discarding crucial information. </p><p>We aim at combining previously developed clustering algorithms in machine learning and topological data analysis to extract useful information such as the number of clusters in an ensemble. Given the chaotic behaviour of the atmosphere, machine learning techniques can provide relevant results even if no, or very little, a priori information about the data is available. In addition, topological methods that analyse the shape of the data can make results explainable.</p><p>Given an ensemble of univariate time series, a graph is generated whose edges and vertices represent clusters of members, including additional information for each cluster such as the members belonging to them, their uncertainty, and their relevance according to the graph. In the case of multimodality, this approach provides relevant and quantitative information beyond the commonly used mean and standard deviation approach that helps to further characterise the predictability.</p>

Manifold Learning for Medical Image Registration, Segmentation, and Classification

2012 ◽  
pp. 351-372 ◽  
Author(s):  
Paul Aljabar ◽  
Robin Wolz ◽  
Daniel Rueckert
Keyword(s):  
Image Analysis ◽  
Image Registration ◽  
Manifold Learning ◽  
Medical Image ◽  
Biomarker Discovery ◽  
Dimensional Space ◽  
Single Point ◽  
Machine Learning Techniques ◽  
Local Linear Embedding ◽  
Learning Techniques

The term manifold learning encompasses a class of machine learning techniques that convert data from a high to lower dimensional representation while respecting the intrinsic geometry of the data. The intuition underlying the use of manifold learning in the context of image analysis is that, while each image may be viewed as a single point in a very high-dimensional space, a set of such points for a population of images may be well represented by a sub-manifold of the space that is likely to be non-linear and of a significantly lower dimension. Recently, manifold learning techniques have begun to be applied to the field of medical image analysis. This chapter will review the most popular manifold learning techniques such as Multi-Dimensional Scaling (MDS), Isomap, Local linear embedding, and Laplacian eigenmaps. It will also demonstrate how these techniques can be used for image registration, segmentation, and biomarker discovery from medical images.

New Trends of Image Analysis in the Medical Field

1988 ◽  
Vol 27 (02) ◽  
pp. 53-57 ◽  
Author(s):  
J. Dengler ◽  
H. Bertsch ◽  
J. F. Desaga ◽  
M. Schmidt
Keyword(s):  
Image Processing ◽  
Cancer Research ◽  
Image Analysis ◽  
A Priori ◽  
Research Work ◽  
A Priori Knowledge ◽  
German Cancer Research ◽  
Medical Field ◽  
New Approach ◽  
Biological Field

SummaryImage analysis with the aid of the computer has rapidly developed over the last few years. There are many possibilities of making use of this development in the medical and biological field. This paper is meant to give a rather general overview of recent systematics regarding the existing methodology in image analysis. Furthermore, some parts of these systematics are illustrated in greater detail by recent research work in the German Cancer Research Center. In particular, two applications are reported where special emphasis is laid on mathematical morphology. This relatively new approach to image analysis finds growing interest in the image processing community and has its strength in bridging the gap between a priori knowledge and image analysis procedures.

Neural network classifiers under changing a priori conditions

1993 ◽  
Vol 29 (6) ◽  
pp. 527 ◽  
Author(s):  
N. Weymaere ◽  
J.-P. Martens
Keyword(s):  
Neural Network ◽  
A Priori ◽  
Neural Network Classifiers

Image analysis based on change point problem for random processes with the absence of a-priori data

1997 ◽  
Author(s):  
Salavat T. Kussimov ◽  
Albert H. Sultanov ◽  
Nail K. Bakirov ◽  
Sergey V. Dyblenko
Keyword(s):  
Image Analysis ◽  
Change Point ◽  
A Priori ◽  
Random Processes ◽  
Point Problem ◽  
Change Point Problem

scholarly journals Semantic Image Analysis for Intelligent Image Retrieval

2015 ◽  
Vol 48 ◽  
pp. 192-197 ◽  
Author(s):  
Anuja khodaskar ◽  
Siddarth Ladhake
Keyword(s):  
Image Analysis ◽  
Image Retrieval ◽  
Semantic Image Analysis

scholarly journals Use of decision-tree induction for process optimization and knowledge refinement of an industrial process

1994 ◽  
Vol 8 (1) ◽  
pp. 63-75 ◽  
Author(s):  
A. Famili
Keyword(s):  
Decision Tree ◽  
Knowledge Base ◽  
Process Optimization ◽  
Electrochemical Machining ◽  
Industrial Process ◽  
Intelligent Manufacturing ◽  
Domain Model ◽  
Machine Learning Techniques ◽  
Decision Tree Induction ◽  
Learning Techniques

AbstractDevelopment of expert systems involves knowledge acquisition that can be supported by applying machine learning techniques. The basic idea of using decision-tree induction in process optimization and development of the domain model of electrochemical machining (ECM) is presented. How decision-tree induction is used to build and refine the knowledge base of the process is also discussed.The idea of developing an intelligent supervisory system with a learning component [Intelligent MAnufacturing FOreman (IMAFO)] that is already implemented is briefly introduced. The results of applying IMAFO for analyzing data from the ECM process are presented. How the domain model of the process (electrochemical machining) is built from the initial known information, and how the results of decision-tree induction can be used to optimize the model of the process and further refine the knowledge base are shown. Two examples are given to demonstrate how new rules (to be included in the knowledge base of an expert system) are generated from the rules induced by IMAFO. The procedure to refine these types of rules is also explained.

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