Classification of malignant melanoma and Benign Skin ...ijarece.org/wp-content/uploads/2017/06/IJARECE-VOL-6-ISSUE-6-538-54… · Malignant Melanoma and Benign.Malignant melanoma

ISSN:2278 – 909X International Journal of Advanced Research in Electronics and Communication Engineering

(IJARECE)

Volume 6, Issue 6, June 2017

538 All Rights Reserved © 2017 IJARECE

Classification of malignant melanoma and Benign Skin

Lesion by Using Back Propagation Neural Network and

ABCD Rule

1Dr. A.Rajesh ,

2Dr.E.Mohan and Mr.M.Sivakumar

1Associate Professor, Department of ECE, CVR College of Engineering, Hyderabad

2Principal, P.T.Lee.C.N.C.E.T, Kanchipuram, Tamilnadu.

3Research Scholar,Shri JJT University,Rajasthan.

Abstract

Human Cancer is a standout amongest the

most unsafe illnesses which is for the most part brought

about by hereditary insecurity of various sub-atomic

modifications. Among many types of human disease,

skin tumour is the most widely recognized one. To

recognize skin tumour at an early stage we will think

about and break down them through different methods

named as segmentation and feature extraction. Here, we

center threatening melanoma skin disease, (because of

the high grouping of Melanoma-Hier we offer our skin,

in the dermis layer of the skin) location. In this, We

utilized our ABCD govern dermoscopy innovation for

harmful melanoma skin malignancy location. In this

framework distinctive stride for melanoma skin injury

portrayal i.e, to begin with, the Image Acquisition

Technique, pre-processing, segmentation, characterize a

component for skin Feature Selection decides sore

portrayal, grouping strategies. In the Feature extraction

by advanced picture preparing technique incorporates,

Asymmetry recognition, Border Detection, Colour, and

Diameter detection and furthermore we utilized LBP

for extract the texture based features. Here we

proposed the Back Propagation Neural Network to

classify the benign or malignant stage.

Index Terms – BPNN, ABCD Rule,Skin Cancer.

I. INTRODUCTION

The event of skin disease in all locales of the world

has risen consistently in the course of the most recent couple of decades because of changes in the pattern

of sun presentation of the populace. Skin Cancer is

exceptionally normal and records for more than 20%

of all growth enrollments. It can be classified into

Malignant Melanoma and Benign.Malignant

melanoma is a forceful kind of malignancy, which

starts from melanocytes situated in the epidermis of

the skin. In spite of the fact that skin diseases are the

most common tumors, they represent just 2% of all

cancer deaths and of those threatening melanoma is

responsible of more than 80%. Early conclusion of melanoma aides in diminishing the bleakness and the

cost of treatment [1, 2].A computer aided diagnostic

system based on new texture shape feature coding is

used to classify the masses is explained in [12]. The

mass classification on mammogram presents a great

challenge for design of computer aided diagnostic

systems due to the complexity of massbackground

and its mammographic characteristics.SVM is

also used for the classification.


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Fig.1: Classification of skin cancer

Melanoma skin disease is the most hazardous type of

malignancy chiefly found in light-cleaned populace.

It can be lethal if not treated at early stage. We as a

whole know well that early identification and

treatment of skin disease can lessen the mortality and

dreariness of patients. In this way, to distinguish skin

growth at early stage Digital Dermoscopy is

considered as a standout amongst the best weapon

which is utilized to recognize and group skin-tumor.

It is non-intrusive in vivo strategy, helps the clinician

in melanoma recognition in its initial stage. This

additionally incorporates dermoscopy, add up to body

photography, computerized indicative framework and

reflectance confocal microscopy.PC innovation

assumed a crucial part in medicinal field. This fills in

as a medicinal choice bolster broadly spread and

unavoidable over an extensive variety of therapeutic

region, for example, gastroenterology, malignancy

investigate, hart ailments, mind tumors and so on.

II. Skin cancer detection through image

processing

According to late research it is conceivable to

perceive skin tumor from pictures utilizing regulated

procedures, for example, counterfeit neural systems

and fluffy frameworks and in addition with highlight

extraction methods. There are likewise numerous

different procedures, names as k-closest neighbors

(k-NN) that additionally amass pixels in light of their

similitudes where each component picture can be

utilized to characterize the ordinary/anomalous

pictures.

In this manner, to distinguish skin growth at early

stage, picture handling strategy is utilized. Picture

preparing is non-costly system. Picture handling is

any type of flag preparing for which the info is a

picture, for example, a picture taker video outline; the

yield of picture preparing might be either a picture or

an arrangement of attributes or parameters identified

with the picture. A picture preparing system includes

regarding the picture as a two-dimensional flag and

applying standard flag handling procedures to it.

Picture handling are PC representation and PC vision.

In PC representation, pictures are made physically

from physical models of articles, conditions, and

lighting, from common scenes. Picture Processing

frames center research region inside building and

software engineering disciplines as well. To

distinguish the edges of a question in a picture scene

is a critical part of the human visual framework; it

gives data on the fundamental topology of the protest

which is utilized to get an interpretative match. Or,

then again we can state that, the division of a picture

into a complex of edges is a critical idea for protest

recognizable proof. There are numerous other

preparing strategies that can be connected for this

reason. Yet, our primary intention is to choose which


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protest limit every pixel in a picture falls inside and

which abnormal state imperatives are essential. In

identification of skin growth through picture

handling, division assumes most critical part to

analyze picture appropriately as it influences the

precision of the resulting steps. Appropriate division

is not a simple assignment on account of the immense

verities of the sizes, injury shapes, and hues

alongside various skin sorts and surfaces.

The ABCD administer is a very much perceived

standard utilized for order of dermatological pictures

to favorable or dangerous [3, 4]. ABCD remains for

the accompanying components: A (asymmetry), B

(border), C (color), and D (Diameter or Differential

structures) [5-7].

Notwithstanding for experienced dermatologists, it

might be hard to make amend determination in light

of the fact that numerous side effects look

fundamentally the same as each other, in spite of the

fact that they are brought on by various sicknesses.

The dermatologists consequently require PC helped

indicative instruments (CADT) that investigate the

points of interest, for example, shading, estimate,

thickness of the skin changes, shape, and so forth and

offer recommendations for viable analysis.

A few CADTs have been proposed in the writing [8-

13]. Such devices could increment symptomatic

exactness and empower putting away of pictures with

demonstrative data for further examinations or

formation of new strategies for finding. They can

enhance the speed of skin malignancy determination

which works as indicated by the malady side effects.

In spite of the fact that there are numerous PC helped

symptomatic instruments, there is by all accounts a

considerable measure of space for further change.

This paper intends to assemble a CADT utilizing

outspread premise work organize for ordering

thedermoscopic skin injuries into favorable or

harmful melanoma. The paper includes four

segments. The primary segment gives the

presentation, the second area recommends the

proposed CADT (PC), the third segment examines

the outcomes and the forward segment finishes up.

III. System Analysis

(a) Local binary patterns

Local Binary Pattern (LBP) is a straightforward yet

exceptionally proficient surface administrator which

names the pixels of a picture by thresholding the

area of every pixel and considers the outcome as a

paired number. Because of its discriminative power

and computational effortlessness, LBP surface

administrator has turned into a prominent approach

in different applications. It can be viewed as a

binding together way to deal with the customarily

dissimilar factual and basic models of surface

investigation. Maybe the most essential property of

the LBP administrator in certifiable applications is its

power to monotonic dim scale changes brought on,

for instance, by brightening varieties. Another

critical property is its computational effortlessness,

which makes it conceivable to break down pictures

in testing continuous settings.


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Fig.2: Description of facial expressions with local

binary patterns.

(b) FNN classifier

The classifier is presented on the basis of

themodification of the typical four-layer forward

FNN. The analysis layer is introduced so that it

becomes a five-layer network, the architecture of

which is shown in fig 1. Its input-output relations

among layers and the corresponding symbols are

described as follows:

Input layer: nxpneurons, each relates toInput xji,j=

1,2.. . n,i= 1,2.. .p, the whole input b,;i} is a two-

dimension matrix .

Received data + WVD PCA

Analysis layer: h Xpneurons, the output of

FNN output

each is f g i ,g = 1,2.. .h ,i= 1,2.. . p

The classifier is presented on the basis of the

modification of the typical four-layer forward FNN.

The analysis layer is introduced so that it becomes a

five-layer network, the architecture of which is

shown in fig 1. Its input-output relations among

layers and the corresponding symbols are described

as follows:

Input layer: nxpneurons, each relates to

inputxji,j= 1,2.. . n,i= 1,2.. .p, the whole input b,;i} is

a two-dimension matrix .

Received data + WVD PCA

Analysis layer: h Xpneurons, the output of

FNN outputeach is f g i ,g = 1,2.. .h ,i= 1,2.. . p

Inference layer: multiplying inference rule is

used,namely

µk = π µki

Fig.4: The architecture of the FNN

Defuzzification layer: one neuron, its output is

y = ∑k ukvk

where the simplified centraldefuzzication

processing is adopted.

(C) KNN classifier

We have utilized k-closest neighbor approach as a

classifier in this work. A protest is grouped by a

larger part vote of its neighbors, with the question

being doled out to the class which is most normal

among its k closest neighbors.The inspiration for this


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classifier is that examples which are near each other

in the element space are probably going to have a

place with a similar example class.

Fig.5 (a) Input images (b) Segmented images

The neighbors are taken from an arrangement of tests

for which the right characterization is known. It is

regular to utilize the Euclidean separation, however

other separation measures, for example, the City

square, Cosine separations could be utilized. In this

work we have utilized three diverse separation

measures viz., Euclidean, City piece and Cosine

remove measure to concentrate the impact on

arrangement precision.

IV. Proposed system

(a) GLCM Texture Features

A GLCM is a histogram of co-happening

grayscale values at a given balance over an

image.Samples of two distinct surfaces are

separated from a picture: verdant regions and sky

territories. For each fix, a GLCM with a level

balance of 5 is processed. Next, two components

of the GLCM networks are processed: disparity

and relationship. These are plotted to delineate

that the classes frame bunches in feature space.

In a typical classification problem, the final step

(not included in this example) would be to train a

classifier, such as logistic regression, to label

image patches from new images.

(b) ABCD Rule

To compute the ABCD score, the 'Asymmetry,

Border, Colors, and Dermoscopic structures criteria

are surveyed semiquantitatively.

Each of the criteria is then increased by a given

weight variable to yield an aggregate

dermoscopyscore (TDS).TDS values under 4.75

demonstrate a benevolent melanocytic injury, values

in the vicinity of 4.8 and 5.45 show a suspicious sore,

and estimations of 5.45 or more noteworthy are

profoundly suggestive of melanoma.

1)Asymmetry

To survey asymmetry, the melanocytic sore is

separated by two 90º axes that were situated to


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deliver the most minimal conceivable asymmetry

score. On the off chance that both dermocopically

indicate topsy-turvy forms with respect to shape,

hues as well as dermoscopic structures, the

asymmetry score is 2. On the off chance that there is

asymmetry on one pivot just, the score is 1.

2)Border The lesion is isolated into eighths, and the shade

example pigment pattern is evaluated. Inside each

one-eighth section, a sharp, unexpected cut-off of

shade example at the fringe gets a score 1.

Conversely, a steady, ill defined cut-off inside the

fragment gets a score of 0. In this way, the maximum

border score is 8, and the minimum score is 0.

The lesion is divided into eighths, and the pigment

pattern is assessed . Within each one-eighth

segment,asharp,abrupt cutoff of pigment pattern at the

periphery receives a score 1.

one-eighth segment, a sharp, abrupt cut-off of

pigment pattern at the periphery receives

a score 1. In contrast, a gradual, indistinct cut-off

within the segment receives a score

of 0. Thus, the maximum border score is 8, and the

minimum score is 0.

3) Color

Six different colors are counted in determining the

color score: white, red, light brown, dark brown, blue-

gray, and black. For each color present, add +1 to

thescore. White should be counted only if the area is

lighter thantheadjacent skin.

The maximum color score is 6, and the minimum

score is 1.

Dermoscopic structures:

Evaluation of dermoscopic structures focuses on 5

structural features: network, structureless (or

homogeneous) areas, branched streaks, dots, and

globules.

The presence of any feature results in a score +1

Structureless (or homogenous) areas must be larger


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than 10% of the lesion to be considered present.

Branched streaks and dots are counted only when

more than two are clearly visible. The presence of a

single globule is sufficient for the lesion to be

considered positive for globules.

Block diagram of proposed system:

One of the most popular NN algorithms is back

propagation algorithm. BP algorithm could be broken

down to four main steps. After choosing the weights

of the network randomly, the back propagation

algorithm is used to compute the necessary

corrections. The algorithm can be decomposed in the

following four steps:

i) Feed-forward computation

ii) Back propagation to the output layer

iii) Back propagation to the hidden layer

iv) Weight updates

This is unpleasant and essential equation for BP

calculation. There are some varieties proposed by

other researcher yet Rojas definition appears to be

very precise and simple to take after. The last stride,

weight updates is going on all through the calculation.

BP calculation will be clarified utilizing exercise case

from figure.Table.1

Table.1 Pattern data for AND

n0,0 n0,0 Output n2,0

1 1 1

1 0 0

0 1 0

0 0 0

β = Learning rate = 0.45

α = Momentum term = 0.9

f(x) = 1.0 / ( 1.0 + exp (-x) )

NN on above figure has two hubs (N0,0 and N0,1) in

info layer, two hubs in shrouded layer (N1,0 and

N1,1) and one hub in yield layer (N2,0). Input layer

hubs are associated with concealed layer hubs with

weights (W0,1-W0,4). Concealed layer hubs are

associated with yield layer hub with weights


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(W1,0and W1,1). The qualities that were given to

weights are taken haphazardly and will be changed

amid BP cycles. Table with info hub values and

fancied yield with learning rate and energy are

additionally given in figure 5. There is likewise

sigmoid capacity equation f(x) = 1.0/(1.0 + exp(−x)).

Demonstrated are figurings for this straightforward

system (computation for instance set 1 will be

appeared (input estimations of 1 and 1 with yield

esteem 1). In NN preparing, all illustration sets are

figured yet rationale behind estimation is the same.

IV. Experimental Results

In this paper we analysis the malignant melanoma and

Benign Skin Lesion by Using Back Propagation

Neural Network and ABCD Rule first we taken input

image. The input image is given.

Fig.6:Input image

Fig 7: Colour Conversion Input image

Fig.7 Represent the colour conversion of input image.

After conversion of the input image is given into the

LBP.

Fig.8: LBP applied input image

After conversion of the input image is given into the

LBP. Inthe next block we applying GLCM feature

extraction it is completed is shows in fig 9.

Fig.9:GLCM features

We applying threshold its create the mask of

the input images.It is present in fig 6.


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Fig.10:Threshold image

Contour detection:

Fig.11.(a) Symmetry detection,(b) Region

growing,(c) Edge detection

Fig.12: NN training completed

Fig.13(a): ABCD calculation

Fig.13 (b): ABCD value calculation

Finally we calculate the ABCD value calculated it

shows in command window the values is given

below,

Irregularity index A(Ira): 0.0273


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Irregularity index B (Ira):2.5305

Irregularity index C (Ira):1.1125

Irregularity index D (Ira):109.8154

Solidity: 0.6613

Bounding box:

0.5000, 0.5000, 271.0000, 186.0000

Centroid:

136.5471, 93.1910

Orientation: -1.7634

Sensitivity: 85.7143

Specificity: 60

Accuracy: 75

Fig.14: performance plot

Fig.15: Analysis

We analyse the performance (accuracy and

sensitivity) of our methods. Finally we find cancer

detected or not .if detected means fig.15 will shows

otherwise the normal dialog box will showed .

VII. Conclusion

Skin disease is a standout amongst the most incessant

sorts of malignancy around the world.

Fundamentally, there are two sorts of skin tumour

called threatening melanoma and non-melanoma.

melanoma skin tumour (MSC) is the most unsafe

type of disease basically found in light-cleaned

populace. The point of our work is to recognize skin

disease at an early stage with the assistance of two

strategies i.e. highlight extraction and division. By

and large, there are four phases named as-division,

highlight extraction, obtaining and characterization.

Among these division is a standout amongst the best

strategies. It is ordered into three classifications i.e.

thresholding, edge form based and district based. We

will utilize thresholding technique to accomplish

better outcome. This technique depends on otstu

strategy which naturally distinguishes the picture.

This technique gives better outcome a decent

difference amongst sore and skin.

VIII. References

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[2] Loescher LJ, Janda M, Soyer HP, Shea K, Curiel-

Lewandrowski C. Advances in skin cancer early detection and

diagnosis. InSeminars in oncology nursing, WB Saunders, 2013;

29(3):170-181.

[3] Almaraz-Damian JA, Ponomaryov V, Rendon-Gonzalez E.

Melanoma CADe based on ABCD Rule and Haralick Texture

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dermoscopic images. Skin Research and Technology,

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[5] Mete M, Sirakov NM. Optimal set of features for accurate skin

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Documents

Classification of malignant melanoma and Benign Skin ...ijarece.org/wp-content/uploads/2017/06/IJARECE-VOL-6-ISSUE-6-538-54… · Malignant Melanoma and Benign.Malignant melanoma