BREAST CANCER DETECTION BASED ON HYBRID FEATURES USING MACHINE LEARNING CLASSIFICATION TECHNIQUES
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Author:
AYAZ AHMED HASHMI
Citable URI :
https://vspace.vu.edu.pk/detail.aspx?id=337
Publisher :
Virtual University
Date Issued:
7/4/2020 12:00:00 AM
Abstract
The breast cancer in the women is most commonly diagnosed type of cancer. The mortality rate
can be reduced if proper and early breast cancer treatment can be made. Masses and
microcalcification contain very important diagnostic information in breast cancer. There is great
variation in masses and micro-calcifications so, radiologists face difficulties in proper diagnosis
of the breast cancer. Researchers in the past developed efficient systems based on computer
aided diagnostic (CAD) systems. Moreover, relevant feature extraction plays a vital role in
proper diagnostic and prognostic. Based on the diverse nature and variations in the breast cancer
mammograms, we propose hybrid feature extraction approach including morphological, entropybased
features,
elliptic
Fourier
descriptors
(EFDs),
texture
and
scale
invariant
feature
transform
(SIFT).
For improving the detection accuracy based on the extracted features, we applied
machine learning classifiers including Support vector machine (SVM) alongwith its kernels such
as Gaussian, radial base function (RBF), polynomial; Naïve Bayes and Decision tree (DT). The
validation was measured using 10-fold cross validation (CV) system. For a performance
evaluation, we computed different metrics including specificity, sensitivity, false positive rate
(FPR), negative predictive value (NPV), positive predictive value (PPV), and area under the
receiver operating curve (AUC). Both combination and single feature are used as an input for
classifiers. The results reveal that both single and combination of features provides higher
detection results. Thus, the new feature extracting approach is more robust in early detection of
breast cancer.
URI :
https://vspace.vu.edu.pk/details.aspx?id=337
Citation:
Hashmi,A(2019),BREAST CANCER DETECTION BASED ON HYBRID FEATURES USING MACHINE LEARNING CLASSIFICATION TECHNIQUES,VIRTUAL UNIVERSITY OF PAKISTAN.(Lahore,Pakistan).
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Final Version
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