Rough Sets in Hybrid Intelligent Systems For Breast Cancer Detection

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Oct 29, 2013 (3 years and 5 months ago)

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WRSTA, 13 August, 2006

Rough Sets in Hybrid
Intelligent Systems For Breast
Cancer Detection





By


Aboul Ella Hassanien


Cairo University, Faculty of Computer and Information, IT Dept.


abo@cba.edu.kw
email:






WRSTA, 13 August, 2006

Outline


Introduction


Digital mammography


Hybrid intelligent systems


Objective


What is Mammogram
?



Mammogram Analysis Framework


Pre
-
processing phase


Segmentation


Feature Extraction phase


Feature Representation phase


Generated Rules phase


Classification phase



Hybrid Intelligent System


Pre
-
processing Algorithm


Fuzzy Image Processing


Rough Set data analysis


Rough neural Classifier


Evaluation


Results


Conclusion and Future Work

WRSTA, 13 August, 2006

Introduction


According to the National Cancer Institute:


Breast cancer is the leading cause of

cancer deaths in women
today and it is the most common type of cancer in women.


Each year about
180
,
000
women in the United States
develop breast cancer, and


About
48
,
000
lose their lives to this disease.


It is also reported that a woman's lifetime risk of developing
breast cancer is one in eight.


Currently,

digital

mammography

is

one

of

the

most

promising

cancer

control

strategies

in

earliest

stages
.

WRSTA,
13
August,
2006

What is a mammograms?


A mammogram

is a
special kind of X
-
ray
that allows the doctor to
see into the breast
tissue


WRSTA,
13
August,
2006

Introduction




Hybridization of intelligent systems

is


A promising research field of modern artificial intelligence concerned with the
development of the next generation of intelligent systems.


A fundamental stimulus to the investigations of Hybrid Intelligent Systems (HIS) is the
awareness in the academic communities that combined and integrated approaches will be
necessary if the remaining tough problems in artificial intelligence are to be solved.


Recently, hybrid intelligent systems are becoming popular due to their capabilities in
handling many real world complex problems, involving imprecision, uncertainty and
vagueness, high
-
dimensionality.


A hybrid intelligent system
is


one that combines at least two intelligent technologies. For example,


Combining a neural network with a fuzzy system results in a hybrid neuro
-
fuzzy system.


Combining a neural network with a rough system results in a hybrid neuro
-
rough system. Etc.


The combination of probabilistic reasoning, fuzzy logic, neural networks and
evolutionary computation forms the core of
soft computing
, an emerging approach
to building hybrid intelligent systems capable of reasoning and learning in an
uncertain and imprecise environment.


WRSTA,
13
August,
2006

Intelligent Systems

Rough Sets

Fuzzy Logic

Neural

Networks

Evolutionary

Algorithms

Chaos & Fractals


Belief


Networks


The primordial soup

WRSTA,
13
August,
2006

Fuzzy Logic :

the algorithms for dealing with
imprecision and uncertainty

Neural Networks
: the machinery for learning
and function approximation with noise

Evolutionary Algorithms

: the algorithms for
reinforced search and optimization

RS

Rough


Sets

uncertainty arising from the
granularity in the domain of

discourse

Different methods = different roles

WRSTA,
13
August,
2006

Comparison of Expert Systems, Fuzzy Systems,

Neural Networks and Genetic Algorithms

WRSTA,
13
August,
2006

Objective


Introduce a
rough neural

intelligent approach for:


Rule generation and image classification.


An application of breast cancer imaging has been
chosen and hybridization of intelligent computing
techniques has been applied to see their ability and
accuracy to classify the breast cancer images into two
outcomes:


malignant cancer or benign cancer.


Computer
-
based to
assist

radiologists in
mammography classification of breast cancer
images (
Computer Aided Diagnosis System
)

WRSTA,
13
August,
2006

Mammogram Analysis Framework

WRSTA,
13
August,
2006

Mammogram Analysis Framework


Pre
-
processing phase


Fuzzy theory


Enhancement


Segmentation: Region of Interest (ROI)


Region Boundary Enhancement


Feature Extraction phase


Statistical features


concurrence Matrix


Rough Sets Data Analysis


Feature representation


Rough information system


Reduct generation


Rule generation


Classification phase


Rough neural classifier


Evaluation

WRSTA,
13
August,
2006

Pre
-
Processing


Fuzzy theory


Mammograms are images that are difficult to
interpret; therefore, techniques are needed to:


Enhance the quality of these images for a better
interpretation.


For this purpose,
a pre
-
processing phase

of the images is
adopted to improve the quality of the images and to make
the feature extraction phase more reliable.



It contains several processes;


to enhance the contrast of the whole image;


Fuzzy histogram hyperbolization algorithm (FHH)


to extract the region of interest;


Modified Fuzzy c
-
mean clustering algorithm


to enhance the edges surrounding the region of interest.


Fuzzy histogram hyperbolization algorithm (FHH)

WRSTA,
13
August,
2006

Feature Extraction


Once the pre
-
processing was completed,
features relevant to region of interest
classification are extracted, normalized and
represented in a database as vector values


Gray level co
-
occurrence matrix (GLCM)


Energy, entropy, contrast and inverse difference moment.



WRSTA,
13
August,
2006

Rough Sets Data Analysis


Create decision table


Compute some reduct
with minimal number of
attributes.


Significance of
attributes: calculate the
weight of the attributes.


Rule Generation


Rule Evaluation


WRSTA,
13
August,
2006

Rough neural network: rough neuron


WRSTA,
13
August,
2006

Results (Enhancement)

WRSTA,
13
August,
2006

Results (Segmentation)

WRSTA,
13
August,
2006

WRSTA,
13
August,
2006

Average Execution time

WRSTA,
13
August,
2006

Number of generated rules and
classification accuracy

WRSTA,
13
August,
2006

Conclusion


Introducing a hybrid scheme that combines the advantages of different
soft computing techniques for breast cancer detection.


Fuzzy sets is used as a pre
-
processing techniques to


enhance the contrast of the whole image; to


extracts the region of interest and then to


enhance the edges surrounding the region of interest.


Then, subsequently extract features from the extracted regions
characterizing the underlying texture of the interested regions.


Feature extractions acquired in this work are derived from the gray
-
level co
-
occurrence matrix.


A rough set approach to attribute reduction and rule generation has been
used.


Rough neural networks were designed for discrimination for different regions
of interest to test whether they are cancer or nun
-
cancer.


The results proved that the soft computing techniques are very
successful and has high detection accuracy.