Detection of colon cancer by using SVM classifier technique

INTERNATIONAL JOURNAL FOR RESEARCH & DEVELOPMENT IN

TECHNOLOGY Volume-6, Issue-4 (Nov-16)

ISSN (O) :- 2349-3585

All rights reserved by www.ijrdt.org

77

Detection of colon cancer by using SVM classifier technique

__________________________________________________________________________________________

Pankita Thakur1, Ruchi Singh

2

1M.Tech student,

2Assistant professor

1,2 Department of Electronics communication Engineering

1,2 LR College of Engineering and Technology, Solan (H.P)-173223

ABSTRACT: Colon cancer is a life threatening disease.

Colon cancer is cancer of the large intestine (colon), the

lower part of your digestive system. Rectal cancer is cancer

of the last several inches of the colon. Together, they're often

referred to as colorectal cancers. Most cases of colon cancer

begin as small, noncancerous (benign) clumps of cells called

adenomatous polyps. Over time some of these polyps become

colon cancers. In this paper a colon cancer detection and

classification system has been designed and developed. In

our proposed method feature extraction is done by three

distinct techniques and these are GLCM, IH and SFM.

Support vector machine is used as a classifier for the

detection of cancerous and non cancerous images.

Keywords-Feature extraction using GLCM,IH,SFM,

detection using SVM.

INTRODUCTION

Cancer is most dreadful & life threatening stroke to human

beings globally among various diseases. Cancer is second

largest stroke in India that is responsible for maximal fatality

around of 0.3 millions death every year [4].Abnormal

expansion of cells developed inside human body called as

tumor [2]. Colon & rectum is the last portion of tube which

extends from mouth to anus. Foods enter in mouth where it

chomp and then absorb. It then travels over the esophagus and

into stomach. In stomach, food is converting in small

fragments and then it enters into small intestine by carefully

and in a controlled manner. In small intestine, finally digestion

of the food and consumption of the nutrients occurs that

contained in the food. Food which does not digested &

absorbed enters in the large intestine or colon and finally in

the rectum. A large intestine was around six feet large in

length. Large intestines act mainly as storehouse facility for

the waste product. Also the additional water, some vitamins

and salts are further removed. The resting undigested food,

decaying cell from lining of intestines, also a large numbers of

bacteria’s stored in colon and after then they periodically leave

into rectum. Arrivals of these materials into rectum begin

bowel movement that vacant the colonic materials from the

body as a stool. Most of large intestines rests insides abdomen

cavity is called peritoneal cavity. Parts of colon are capable to

move freely inward peritoneal cavity just as undigested food

passing over it. As colon heads approaching rectum and it

become fixed to tissues that are beyond peritoneal cavity and

area called as retro peritoneum. The ending part of large

intestine, part that resides in the retro peritoneum, is called

rectum. Unlike rest of colon, rectum is fixed in a place through

the tissues that are surrounding it. Due to of its fixed location,

treatment of the rectal cancers is often different than treatment

for the rest cancer of colon. Cancers of colon & rectum starts

when the normal process of replacement of cells of the lining

goes awry. A mistake in the mucosal cell separation occurs

frequently. As these unusual cells are growing and separate,

they can approaches to growths in colon that is called as

polyps. Polyps alter in types, but many polyps are

precancerous tumor which growing slowly over duration of

years, and it does not invade. As the polyps grow, some

additional genetic variations further destabilized cells and they

can make cells more unusual. When these precancerous

tumors change their and spreads into another layers of large

intestine (such as submucosa or muscular layer), then the

precancerous polyps has becomes cancerous. In many cases

the given process is very slow and it taking around 8-10 years

to fully developed from those initial abnormal cells to develop

cancer. Figure 1.1 shows normal and colon cancer image [31].

http://www.medicinenet.com/image-collection/esophagus_picture/picture.htm

http://www.medicinenet.com/the_digestion_process_organs_and_functions/article.htm

http://www.medicinenet.com/feet_facts_quiz/quiz.htm

http://www.medicinenet.com/vitamins_and_calcium_supplements/article.htm

http://www.medicinenet.com/image-collection/intestines_picture/picture.htm

http://www.medicinenet.com/cancer_101_pictures_slideshow/article.htm

Paper Title:- Detection of colon cancer by using SVM classifier technique

ISSN:-2349-3585 |www.ijrdt.org 78

(a)

(b)

Fig. 1.1: Normal and cancerous colon image

Early and proper detection of colon cancer is essential key for

proper treatment. So we require accurate tool for proper

treatment. Detection involves finding the presence of tumor,

segmentation involves the detection of size and location of

tumor and classification involves the detection of stage of

tumor. Now a day’s different computer added tools are used in

medical field. These tools possess a property of quick and

accurate results [2]. In this paper, we used SVM classifier to

detect colon cancer. The rest of the paper is organized as

follows. In section II, the methodology is defined. And

experimental results, different feature parameters are

presented in section III. Section IV is conclusion.

II METHODOLOGY

The proposed methodology of discrimination between

cancerous and non cancerous images is shown in figure 1.2.

The method used two set of images i.e. training and testing set

then uses different steps segmentation, feature extraction and

classification. The work at hands is implemented using Mat

lab version 2013a.

FLOW CHART

The different feature extractions approaches are classified as

follows:

Grayscale Features Extraction Technique

1) GLCM

Number of texture features is extracted from the GLCM.

GLCM is statistical method to finding textures that consider

spatial relationship in the pixels. The GLCM function in the

MATLAB forms GLCM by computing how frequently pixel

with intensity value e occurred in particular structural

relationship of pixel with value f. By default, the spatial

relationship is defined as the pixel of interest and the pixel to

its immediate right (horizontally adjacent), but we can specify

other spatial relationship in between two pixels. Every element

(e and f) in resultant GLCM a combination of the numbers of

time pixel with a value E occur in stated structural relationship

of pixels with the value F in input image.

Table-1

Feature computed from GLCM



Feature Equation

Mean Me = e. Pd

fe

(e, f)

Correlatio

n corr = ef . Pdf e, f − memfe

ax ay

Entropy Entr = Pd

fe

e, f log(Pd e, f )

Contrast

co = n2

Lg−1

n=0

Pd

Lg

f=1

Lg

e=1

e, f e − f = n

Energy Eng = Pd

fe

(e, f)2

Homogen

ity Hom =

1

1 + e + f 2

fe

Pd (e, f)

Sum

Variances Sv = (e− A14)2

2Lg

e=2

. epx+y (e)

2) Intensity Histogram (IH)

First-order texture measure was computed from original

values of an image. It does not examine the relationship with

the neighborhood pixels. Histogram based technique for the

detection of texture is depends on gray scale value

concentrations on all part of image illustrated as histogram.

Features are derived from this technique consists moments

like- average energy, skewness, kurtosis, mean, and standard

deviation. Histogram of an intensity levels are simply a

summary of structural information of an image and individual

pixels were used for the calculation of gray-level histogram.

Therefore, histogram consist first-order analytical information

regarding to image (or about the sub image). These statistics

features are determined by using the given equations.

Table-2

Feature extracted from IH

3) Statistical feature matrix (SFM)

SFM shows the measurements of structural properties of the

pixel pairs at distinct locations. Following are the features that

extracted from SFM are shown in table below-

Table-3

Feature extracted from SFM

Feature

Name

Equation

Coarseness coars =

CF

Ds e,f ∈Msf(e, f)/NS

Periodicity pe =md − mdv

md

Contrast cont =

con2(e, f)

4(e,f)∈Msf

Roughness Rog =

d f x

+ d f(y )

2

Classifier used

Support Vector Machine (SVM)

In 1979 support vector machine was firstly discovered by

vapnik[26]. Support vector machine is learning machine that

used as a tool for the function approximation, classification of

data, etc, because of its generalization capacity it constitute

success in multiple applications. SVM has advantage of the

selection of model automatically which means that the

locations of basis functions and optimal number were

automatically generated during training. Working of SVM was

mostly based on kernels [22]. SVM can use for the pattern

classification which has MLP and RBF networks. The SVM is

the leading technology that including maximal classification

algorithms that are embedded in structural learning theory.

SVM method is using for the classification of non-linear and

linear data. SVM converts original training dataset into high

dimension by use of non-linear mapping. With this newly

generated dimension SVM researches for linear optimal

severing hyperplane. Data from the two classes are separated

by the hyperplane by using a convenient nonlinear mapping

for an adequately high dimension. By using support vectors &

margins SVM determine these hyperplane. SVM implements

classification function by magnifying the margin that classifies

both the class although minimizing classification errors. SVM

can also apply to the different optimization tasks like

regression; classic complication is that of data classification.



Data points were analyzed as being negative or positive and

task is to determine hyper-plane which separates data points

from the maximum margin [25].To construct Support vector

machine classifier parameter & kernel function should be

preferred. Generally used kernel function to the SVM was

RBF because their localized & finite responses across whole

range of the real x-axis. And classification accuracy of RBF

kernel was high; also, [27] the bias values and error rate of

RBF kernel was small as compared to another kernels.

Actual idea behind SVM is to form a hyper plane in between

the data sets to express which class it belongs to. The task is to

train the machine with known data and then SVM find the

optimal hyperplane which gives maximum distance to the

nearest training data points of any class. We consider data

points of the form {(W1, X1), (W2, X2), (W3, X3) ………..

(Wn, Xn)} Where Xn= 1/ -1, a constant denoting the class to

which that point Wn belongs. n = number of sample. Each Wn

is r-dimensional real vector. The task is to find the maximum-

margin hyperplane that divides the points having Xn = 1from

those having Xn = -1. Any hyperplane that satisfy the set of

points W can be written as [15]

Y.W+b=0

(i)

Where b is scalar and Y is r-dimensional Vector. If the

training data are linearly separable, SVM can chose two

hyperplanes that divide the data in a way that have no points

between them, and also have maximum distance between both

hyperplanes[35]. The regions bounded by both hyperplanes

are called "the margin". These equations for both hyperplane

can be defined as

Y.W+b=1

(ii)

Y.W+b=-1w

(iii)

By geometry, the distance between the hyperplane is 2 / │y│.

Now add the following constraint: for each N either.

Y.Wn+b=1

(iv)

Y.Wn+b=-1

(v)

It is equivalent to

Xn(Y.W+b)>=1

(vi)

The classifier written as

f(w) = sign (Y.W+b)

SVM can apply to the different optimization tasks like

regression; classic complication is that of data classification.

SVM has also been used on different real world problems such

as face recognition, cancer diagnosis, microarray gene

expression data analysis, text categorization, glaucoma

diagnosis etc.

EXPERIMENTAL RESULT

Extraction of Different features parameters on fixed data size

of 45 images.



Sr.

No.

A

B

C

D

E

F

G

H

I

J

K

1 1.55 1.46 1.21 0.59 1.34 2.69 10.62 2.28 0.08 2629.49 319.40

2 1.55 1.46 1.21 0.59 1.34 2.69 10.62 2.28 0.08 2629.49 319.40

3 1.55 1.46 1.21 0.59 1.34 2.69 10.62 2.28 0.08 2629.49 319.40

4 1.55 1.46 1.21 0.59 1.34 2.69 10.62 2.28 0.08 2629.49 319.40

5 1.55 1.46 1.21 0.59 1.34 2.69 10.62 2.28 0.08 2629.49 319.40

6 1.87 1.45 1.20 0.37 1.82 1.51 5.60 1.58 0.08 2337.71 331.84

7 1.80 1.24 1.11 0.39 1.73 1.64 6.70 1.42 0.08 2343.30 333.00

8 1.81 1.17 1.08 0.38 1.73 1.60 6.74 1.02 0.08 2386.04 335.65

9 1.81 1.15 1.07 0.37 1.73 1.53 6.26 0.99 0.08 2391.30 335.90

10 1.81 1.16 1.08 0.37 1.74 1.53 6.28 0.90 0.08 2370.92 336.67

11 1.57 0.90 0.95 0.49 1.43 1.75 6.26 0.99 0.09 2720.11 331.09

12 1.57 0.90 0.95 0.49 1.43 1.70 5.94 1.03 0.09 2702.87 331.09

13 1.59 0.93 0.97 0.48 1.46 1.65 5.67 1.03 0.09 2676.62 331.79

14 1.58 0.88 0.94 0.48 1.45 1.64 5.72 0.93 0.08 2665.43 332.96

15 1.59 0.89 0.95 0.48 1.46 1.63 5.67 0.94 0.08 2663.16 332.94

16 2.07 2.42 1.56 0.42 1.81 1.21 3.39 0.79 0.08 2561.13 335.98

17 2.03 2.19 1.48 0.41 1.80 1.20 3.47 0.71 0.08 2546.50 336.81

18 1.88 1.80 1.34 0.45 1.68 1.41 4.33 0.76 0.08 2644.86 335.07

19 1.89 1.91 1.38 0.45 1.69 1.43 4.33 0.79 0.08 2652.14 334.65

20 1.74 1.63 1.28 0.51 1.50 1.65 5.09 0.74 0.09 2880.06 328.49

21 2.24 3.03 1.74 0.34 2.11 1.40 4.24 1.16 0.08 2347.34 334.88

22 2.19 2.85 1.69 0.35 2.08 1.43 4.39 1.08 0.08 2348.69 335.42

23 2.17 2.72 1.65 0.35 2.06 1.43 4.40 0.89 0.08 2357.54 336.75

24 2.20 2.82 1.68 0.35 2.08 1.40 4.25 0.98 0.08 2353.28 336.11

25 2.20 2.77 1.66 0.35 2.08 1.39 4.22 0.83 0.08 2361.15 337.17

26 1.72 1.20 1.09 0.47 1.51 1.27 3.67 0.76 0.09 2703.69 333.88

27 1.72 1.20 1.09 0.47 1.51 1.27 3.67 0.76 0.09 2703.69 333.88

28 1.89 1.89 1.37 0.44 1.74 1.55 4.85 0.70 0.08 2633.97 333.81

29 1.89 1.89 1.37 0.44 1.74 1.55 4.85 0.70 0.08 2633.97 335.81

30 1.89 1.89 1.37 0.44 1.74 1.55 4.85 0.70 0.08 2633.97 335.81

31 1.71 1.43 1.20 0.47 1.61 1.84 6.29 0.91 0.08 2637.52 333.75

32 1.70 1.41 1.19 0.46 1.62 1.93 6.71 0.83 0.08 2637.14 334.54

33 1.71 1.43 1.20 0.46 1.63 1.92 6.49 0.74 0.08 2642.05 335.26

34 1.64 1.22 1.10 0.48 1.55 2.01 7.14 0.81 0.09 2695.42 333.59

35 1.67 1.31 1.14 0.47 1.57 1.98 7.00 0.85 0.09 2681.02 331.51

36 1.32 0.74 0.86 0.72 0.93 3.11 12.97 0.95 0.13 3013.11 275.70

37 1.66 0.98 0.99 0.44 1.57 1.48 4.74 1.16 0.08 2407.29 334.46

38 1.65 0.81 0.90 0.42 1.54 1.17 3.61 0.81 0.08 2387.82 337.20

39 1.59 0.94 0.97 0.49 1.46 1.63 7.11 0.81 0.09 2673.61 334.00

40 1.64 1.02 1.01 0.47 1.52 1.53 4.90 0.77 0.08 2603.78 335.64

41 1.59 1.10 1.05 0.51 1.45 1.90 6.60 1.17 0.09 2742.29 328.45

42 1.56 0.95 0.97 0.51 1.42 1.95 7.30 0.98 0.09 2747.59 330.47

43 1.54 0.86 0.93 0.51 1.38 1.96 7.64 0.92 0.09 2756.04 330.90

44 1.56 0.94 0.97 0.51 1.40 1.93 7.26 0.98 0.09 2755.65 330.16

45 1.56 0.92 0.96 0.51 1.40 1.88 7.00 0.92 0.09 2755.36 330.92



Sr.

No.

L

M

N

O

P

Q

R

S

T

U

V

W

X

Y

Z

1 .33 .51 .82 .96 .63 11.03 1.69 5.03 0.41 2.17 -0.06 -0.80 .82 10.18 74.38

2 .33 .51 .82 .96 .63 11.03 1.69 5.03 0.41 2.17 -0.06 -0.80 .82 10.18 74.38

3 .33 .51 .82 .96 .63 11.03 1.69 5.03 0.41 2.17 -0.06 -0.80 .82 10.18 74.38

4 .33 .51 .82 .96 .63 11.03 1.69 5.03 0.41 2.17 -0.06 -0.80 .82 10.18 78.38

5 .33 .51 .82 .96 .63 11.03 1.69 5.03 0.41 2.17 -0.06 -0.80 .82 10.18 74.38

6 .23 .47 .85 .97 .51 12.21 1.20 3.39 0.48 1.53 0.00 -0.90 .86 6.80 84.30

7 .02 .47 .84 .97 .51 12.22 1.18 3.18 0.47 1.38 0.01 -0.91 .86 8.47 70.93

8 .15 .48 .81 .98 .53 12.51 1.08 2.59 0.45 1.00 0.04 -0.95 .87 10.30 61.45

9 .14 .48 .80 .98 .54 12.14 1.07 2.43 0.45 0.97 0.05 -0.95 .87 10.67 59.60

10 .13 .48 .80 .98 .52 12.21 1.09 2.47 0.44 0.88 0.05 -0.96 .87 10.69 60.57

11 .14 .53 .74 .98 .65 11.07 1.47 3.30 0.34 0.97 0.05 -0.92 .85 8.84 82.25

12 .15 .53 .75 .98 .64 11.12 1.47 3.34 0.34 1.01 0.04 -0.92 .85 8.70 82.37

13 .15 .52 .75 .98 .63 11.23 1.46 3.30 0.35 1.00 0.04 -0.93 .85 8.40 84.74

14 .13 .52 .75 .98 .63 11.29 1.43 3.16 0.34 0.92 0.05 -0.94 .86 8.90 78.90

15 .13 .52 75 .98 .63 11.30 1.43 3.16 0.35 0.92 0.05 -0.94 .86 8.81 79.82

16 .11 .51 .76 .99 .60 11.67 1.35 2.82 0.36 0.78 0.06 -0.96 .86 5.89 112.75

17 .10 .51 .76 .99 .60 11.73 1.32 2.69 0.36 0.70 0.07 -0.97 .87 7.09 99.30

18 .11 .52 .74 .99 .62 11.41 1.39 2.88 0.34 0.75 0.07 -0.96 .86 7.86 93.28

19 .11 .52 .74 .99 .63 11.37 1.40 2.93 0.34 0.78 0.06 -0.95 .86 7.09 99.84

20 .11 .56 .69 .99 .68 10.46 1.50 3.16 0.29 0.73 0.07 -0.93 .85 8.84 85.27

21 .17 .48 .82 .98 .50 12.24 1.16 2.87 0.45 1.13 0.03 -0.94 .87 6.05 98.48

22 .15 .48 .82 .98 .50 12.25 1.16 2.79 0.44 1.06 0.04 -0.95 .87 6.76 90.38

23 .13 .48 .80 .98 49 12.25 1.14 2.54 0.43 0.88 0.06 -0.97 .87 7.59 81.59

24 .14 .48 .81 .98 .50 12.25 1.15 2.66 0.44 0.96 0.05 -0.96 .87 6.95 87.76

25 .12 .48 .80 .99 .50 12.25 1.12 2.42 0.43 0.82 0.06 -0.97 .87 7.68 80.96

26 .11 .53 .73 .99 .64 11.20 1.42 2.96 0.33 0.75 0.07 -0.95 .86 8.80 80.50

27 .11 .53 .73 .99 .64 11.20 1.42 2.96 0.33 0.75 0.07 -0.95 .86 8.80 80.50

28 .10 .52 .74 .99 .62 11.46 1.37 2.78 0.34 0.69 0.07 -0.96 .86 7.63 93.97

29 .10 .52 .74 .99 .62 11.46 1.37 2.78 0.34 0.69 0.07 -0.96 .86 7.63 93.97

30 .10 5.2 .74 .99 .62 11.46 1.37 2.78 0.34 0.69 0.07 -0.96 .86 7.63 93.97

31 .13 .52 .75 .98 .62 11.40 1.41 3.09 0.35 0.89 0.05 -0.94 .86 6.91 95.81

32 .12 .52 .75 .99 .62 11.42 1.40 2.97 0.34 0.81 0.06 -0.95 .86 7.58 94.60

33 .11 .52 .74 .99 .62 11.42 1.38 2.85 0.34 0.73 0.07 -0.96 .86 8.19 90.24

34 .12 .53 .74 .99 .64 11.22 1.42 3.01 0.33 0.80 0.06 -0.95 .86 9.40 78.65

35 .12 .52 .74 .98 .63 11.26 1.42 3.05 0.34 0.83 0.06 -0.94 .86 8.45 83.28

36 .14 .68 .50 .98 .81 7.37 1.79 4.20 0.93 0.05 -0.81 -0.81 .77 13.29 73.90

37 .17 .48 .81 .98 .55 12.07 1.31 3.13 1.13 0.03 -0.93 -0.93 .86 9.83 71.81

38 .12 .49 .79 .99 .53 12.18 1.21 2.57 0.80 0.06 -0.97 -0.97 .87 11.71 58.30

39 .12 .52 .74 .99 .63 11.29 1.41 2.99 0.80 0.06 -0.95 -0.95 .86 11.38 63.81

40 .11 .51 .75 .99 .61 11.54 1.37 2.84 0.75 0.07 -0.96 -0.96 .86 11.53 61.43

41 .17 .53 .74 .98 .65 10.92 1.52 3.59 0.34 1.14 0.03 -0.90 .84 7.76 87.19

42 .14 .53 .73 .98 .65 10.96 1.48 3.32 0.33 0.96 0.05 -0.92 .85 9.17 76.25

43 .13 .53 .73 .98 .65 10.95 1.48 3.24 0.32 0.90 0.05 -0.93 .85 11.01 66.21

44 .14 .53 .73 .98 .65 10.93 1.49 3.34 0.33 0.96 0.05 -0.92 .85 9.67 73.53

45 13 .53 .73 .98 .65 10.95 1.48 3.24 0.32 0.90 0.05 -0.93 .85 10.66 70.91



Sr. No. AA AB AC

1 .55 2.37 1

2 .55 2.37 1

3 .55 2.37 1

4 .55 2.37 1

5 .55 2.37 1

6 .49 2.46 1

7 .50 2.44 1

8 .56 2.36 1

9 .56 2.33 1

10 .56 2.34 1

11 .53 2.39 1

12 .54 2.37 1

13 .54 2.37 1

14 .55 2.39 1

15 .55 2.39 1

16 .57 2.33 1

17 .58 2.30 1

18 .62 2.25 1

19 .60 2.30 1

20 .62 2.25 1

21 .56 2.34 1

22 .58 2.31 1

23 .60 2.28 1

24 .57 2.34 1

25 .60 2.28 1

26 .57 2.34 2

27 .57 2.34 2

28 .58 2.32 2

29 .58 2.32 2

30 .58 2.32 2

31 .55 2.36 2

32 .57 2.32 2

33 .61 2.25 2

34 .60 2.26 2

35 .59 2.29 2

36 .58 2.29 2

37 .57 2.33 2

38 .58 2.32 2

39 .59 2.28 2

40 .60 2.28 2

41 .54 2.38 2

42 .56 2.35 2

43 .58 2.32 2

44 .58 2.34 2

45 .60 2.30 2

The feature extraction table shows 30 different features and

features are

A Mean

B Variance

C Standard Deviation

D Energy

E Entropy

F Skewness

G Kurtosis

H Contrast

I Correlation

J Cluster_Prominence

K Cluster_ Shades

L Dissimilarity

M Energy

N Entropy

O Homogenity

P Maximum Probability

Q Sum of Square

R Sum _Avg

S Sum Variance

T Sum Entropy

U DV

V Diff Entropy

W CF1

X CF2

Y FcrS

Z Fcon

AA Fper

AB Frgh

AC Lable

Table-4

Performance Evaluation Table for the SVM classifier

MeanSe

n

MeanS

pe

MeanPP

V

MeanAccura

cy

MeanAU

C

91.67

80

90

87.5

0.85

Fig1.3: ROC Curve for performance evaluation of Sensitivity

Vs specificity

CONCLUSION

The area of disease analysis is continuously developed and it

is a very active field of research. The purpose of current study

was to classify the colon cancer. A novel technique for

classification of colon cancer nodule using SVM classifier has

been proposed here. Various Textural and structural features

used for categorizing cancerous & noncancerous images. The

Results obtained are very supporting; data was tested on SVM

classifier with the RBF kernel getting an accuracy of 87.5%.



In future work we can improve the classification accuracy by

extracting more features and increasing the training data sets.

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Detection of colon cancer by using SVM classifier technique

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