Dynamic rule refinement strategy of associative classifier for effective mammographic image classification
Computer-aided diagnosis (CADx) has gained significant attention in helping radiologists in the interpretation of mammograms to assist in diagnostic decisionmaking. A more effective CADx increases the probability of cure. An effective mammogram classification technique benefit to the research of...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
2016
|
| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/79330/1/FSKTM%202016%2031%20ir.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Computer-aided diagnosis (CADx) has gained significant attention in helping
radiologists in the interpretation of mammograms to assist in diagnostic decisionmaking.
A more effective CADx increases the probability of cure. An effective
mammogram classification technique benefit to the research of computer aided
mammography for a better diagnostic assistance. However, the effectiveness of
classifiers depends on the training data sets that are often small in data size and
static, which does not adapt to changes. The main aim of this thesis is to propose an
effective associative classifier using rule refinement technique that adapts changes in
databases for building an effective CADx model in the classification of mammogram
images.
The classifiers using Association Rule (AR) mining gain popularity compared to
traditional classifiers due to their nature in reflecting close dependencies among
single or multiple features for composing rules with its excellent interpretation. The
existing associative classification techniques that are used in Computer Aided
Diagnosis (CADx) have proved their efficiency in mammogram classification. The
research aims to propose an improved associative classification model with its first
step preprocessing that uses segmentation technique with filter that includes certain
areas of the image for mammogram peripheral enhancement. The feature extraction
is used to extract the most prominent features from mammogram images that
represent various classes of the images to be used by classification techniques. A
feature selection technique named Correlation Feature Selection (CFS) that involves
a heuristic search is adopted for dimensionality reduction of feature space to improve
efficiency and at the same time maintain the effectiveness of classification. The
thesis discovers useful and interesting relations between features and class in the
form of rules to build an efficient associative classifier from a large collection of
mammogram images using association rule mining technique. An Associative
Classifier that uses rules Highest Average Confidence (ACHAvC) is proposed for an
effective classification of mammography. The classifier ACHAvC has achieved high accuracy of 90% and specificity of 90%, however the sensitivity is 78.5% and not
commendable in medical domain.
The effectiveness of an associative classifier depends largely on the generated rules
based on training data. In previous works such as HiCARe, SACMINER, MINSAR,
including ACHAvC the training data have been limited, which may produce the
classification rules that are static and cannot adapt to a changing charecteristic of test
images, as such it may not produce complete and accurate rules for classification.
The classification performance can be further improved if the static rules are updated
dynamically. The availability of radiologist ground truth for every case could be
used to validate the classification result and refine the set of rules generated. A
method Rule Refinement based on Incremental Modification (RRIM) is proposed
that dynamically refines the rules every time when it is validated with the experts
ground truth. As such these refined rules that adapt the changes in the data are then
used for classification to further enhance the performance of the classifier ACHAvC
with a reduced minimal error and with improved prediction accuracy.
The Performance of the proposed methods are evaluated for accuracy, sensitivity and
specificity for the mammogram image data set, taken from the digital database for
mammography from the University of South Florida, Digital Database for Screening
Mammography (DDSM). The proposed method has achieved an overall
classification accuracy of 96%, with sensitivity 92.56% and specificity 96.94% in
testing stage which is comparatively better than the three benchmark approaches
HiCARe, SACMINER, MINSAR that are chosen for the proposed research with
accuracy of (91%, 85%, 79%), sensitivity (95%, 84%, 87%) and specificity (84%,
86%, 67%). |
|---|