Deep Neural Network for Fuzzy Automatic Melanoma Diagnosis

Wiem Abbes and Dorra Sellami

CEM Laboratory, National Engineering School of Sfax, Sfax University, Soukra Street, Sfax 3038, Tunisia

Keywords:

Melanoma, Bag of Words, CAD System, Feature Extraction, Fuzzy C-Means, Deep N eural Network Classiﬁer.

Abstract:

Melanoma is the most serious type of skin cancer. We consider in this paper diagnosing melanoma based on

skin lesion images obtained by common optical cameras. Given the lower quality of such images, we should

cope with the imprecision of image data. This paper proposes a CAD system for decision making about the

skin lesion severity. We ﬁ r st deﬁne the fuzzy modeling of the Bag-of-Words (BoW) of the lesion. Indeed,

features are extracted from t he skin lesion image related to four criteria inspired by the ABCD rule (A sym-

metry, Border, Color, and Differential structures). Based on Fuzzy C-Means (FCM), membership degrees are

determined for each BoW. Then, a deep neural network classiﬁer is used for decision making. Based on a pu-

blic database of 206 lesion images, experimental results demonstrate that the fuzziﬁcation of feature modeling

presents good results in term of sensitivity (90.1%) and of accuracy (87.5%). A comparative study illustrates

that our approach offers the best accuracy and sensitivity.

1 INTRODUCTION

Melanoma presents the most serious form of skin can-

cer. It is the most speedily developing cancer in the

world (Bickers et al., 2006). The melanoma incidence

continues to increase signiﬁcantly, at a rate rapidly

than that of any of the seven most common cancers

(Ries et al., 2006). Nearly 178,560 melanoma cases

will be diagnosed in 201 8, with approximately 9,320

cases of death (One person dies o f melanoma every

hour) (Facts and Figures, 2018). There are th ree main

types of skin cancer: Basal cell carcinoma, Squamous

cell ca rcinoma, and Melanoma which r e presents only

4% of skin cancers but it is respon sible for 75% of

deaths from skin cancer (Haralick et al., 1973; Ferlay

et al. , 2010).

A diagnosis of skin lesions is performed b y a two-

step procedure. The ﬁrst step is to differentiate be-

tween melanocytic and nonmelanocytic skin lesions

and the second step is to identify b e nign melanocy-

tic lesions from melanoma ba sed on derm oscopy ru-

les. Many de rmoscopy rules are adopted by derma-

tologists : ABCD rule (Asymmetry, Border, Colors,

and Differential struc tures) (Stolz, 1994), seven-point

checklist (Argenziano et al., 1998), Menzies meth od

(Menzies et al., 1996) and CASH algorithm (H e nning

et a l., 2007). These dermoscopy rules allow making

the diagnosis more reproduc ible an d reliable. Despite

using these rules, clinical analysis of ma lignant mela-

noma is still challenging.

In the last decade, various Computer-Aided Diag-

nosis (CAD) systems have be en developed to solve

this problem such as (Celebi a nd Zornberg, 2014; Co-

della et al., 2017; Lopez e t al., 2017; Yu e t al., 2017;

Quang et al., 2017). These existing CAD systems

achieve good results in term of speciﬁcity and of accu-

racy, but they are not satisfactory in ter m of sensiti-

vity. Therefore, the automatic diagnostic process is

still an open problem .

Several previous works are based on dermoscopy

images. Indeed , a dermatoscope is an optical de-

vice that ma nipulates light features to elucidate sub-

surface information. This device is used by derma-

tologists to ob tain an excellent qu ality o f an image.

Therefore, it allows identifying benign and malignant

lesions. Unfortu nately, the use o f dermatoscopes is

limited in Taiwan, USA, and India. Ac cording to r e -

cent survey reporting, only nearly half of dermato-

logists use dermatoscope in these countries (Engas-

ser and Warshaw, 2010; Kuo et al., 2015; Kaliyadan

et al., 2018). In fact, a dermato scope presents an ex-

pensive solution in comparison with optical standa rd

cameras. Accordingly, we consider, in this study, di-

agnosing melanoma based only on skin lesion images

obtained by optical c a meras. Therefore, these images

have poor co ntrast and less saturated colors, adding a

vagueness of image data knowledge.

In the ﬁeld of medical image analysis, fuzzy mo-

deling is adopted to manage the imprecision of ana-

lyzed data. In fact, the fuzzy logic theory is able to

Abbes, W. and Sellami, D.

Deep Neural Network for Fuzzy Automatic Melanoma Diagnosis.

DOI: 10.5220/0007697900470056

In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019), pages 47-56

ISBN: 978-989-758-354-4

describe the qualitative concepts (e.g. poor, medium,

and highly) an d then to cope with the vagueness of ex-

perts’ rules. Thus, it is well-adapted for the modeling

step formulated by experts. To cope with the impre-

cision of extracted data, we adop t the fuzziﬁcation to

model the Bag-of-Words (BoW) of the lesions.

In recent de cades, CAD systems using Deep Neu-

ral Networks (DNN) has been reported to be an ef-

ﬁcient tool for the diagnosis of skin lesions (Binder

et al., 1994; Piccolo et al., 2002; Blum et al., 2004).

In our work, a deep neur al network classiﬁer is used to

identify th e classes of benign common nevi and ma-

lignant melanoma.

In our work, we develop a CAD system for me-

lanoma detection based on fuzzy BoW using DNN

classiﬁer. Firstly, we apply the fuzziﬁcation of featu-

res extracted from preprocessed lesion images using

Fuzzy C-Means (FCM) in order to model the BoW.

The result of this feature modeling step is the mem-

bership degrees of the lesion to each word of the

BoWs. Then, these membership degrees are a pplied

as input to a DNN classiﬁer for dec ision making about

the skin lesion severity.

The remainder of this paper is organized as fol-

lows: In section 2 , we deﬁne related works on skin

lesion classiﬁcation . Then, in section 3, we describe a

general overview of the prop osed CAD system where

a fuzzy modeling is proposed of BoW. Later in section

4, we give details on feature modeling step. Then, in

section 5, we describe deep classiﬁcation. Thus, in

section 6, exp erimental results, based o n public data-

bases, are discussed. Finally, conclusions are dr awn

in section 7.

2 RELATED WORKS

Many studies on automatic diagn osis of melanoma

have been developed in the last few decades where

several methods and techniques have been propo sed.

Most of these previous researches consider only der-

moscopy images (Celebi and Zornberg, 2014; Codella

et al., 2017; Lopez et al., 2017; Yu et al., 2017; Qua ng

et al. , 2017). I n ( Celebi and Zornberg, 2014), auth ors

present an approach for au tomatic estimation of cli-

nically signiﬁcant colors in dermoscopy images. The

database of this stud y is composed of 914 images (272

melanoma s and 642 non-melanoma) obtained fro m

the EDRA Interactive Atlas of Dermoscopy. Based

on color features, the rates of classiﬁcation obtained

are nearly 71.7% for accuracy, 75.8% for speciﬁcity

and 61.6% for sensitivity.

In ( Codella et al., 2017), authors adopted a CNN

model in order to segment and classify the skin le-

sions fo r melanoma detection from dermoscopy skin

lesion images. They yield a n accu racy of %94 on seg-

mentation and an accuracy of 85% on classiﬁcation.

In an other study, Lopez et. al. use a CNN mo del

for melanoma detection. They are based on trans-

fer learning method and they acquired an accuracy

of 81% on dermoscopy images (Lopez et al., 2017).

In (Yu et al., 2017), authors use the fully convolutio-

nal residual network (FCRN) and deep r esidual net-

work (DRN) for segmentation and classiﬁcation of

melanoma . They get accuracy rates of 94% and 85%

for segmentation and classiﬁcation, respective ly. In

(Quang et al., 2017), authors adopt a fully convolu-

tional model to segment skin lesion and use a sim-

ple CNN and VGG-16 model based on transfer lear-

ning for skin lesion classiﬁcation. This work achieves

a segmentation accur a cy of 87% and a classiﬁcation

accuracy of 79%. These previous works are working

on ISIC Archive dataset (Marchetti et al., 2018).

According to the state of art, all the existing met-

hods suffer from sensitivity low values. Such values

result from ina ccuracy mode ling o f melanoma so that

they are confused to benign lesions. In this paper,

we consider improving such modeling by taking into

account the ambiguity of d a ta in a fuz zy framework

modeling. Besides, we will be based in our dataset

on more samples of m e la noma than those used in the

state of the art, let it be half of the samples. Our da-

taset is then balanced. A further improvement of the

accuracy of diagnosis can be obtain e d by app lying a

machine learning decision-making system, which is

able to cop e with the complexity of such a diag nosis

of melanoma. Accordingly, a deep c lassiﬁer is a pp-

lied in decision making.

3 GENERAL OVERVIEW

In Figure.1, we present a general block-diagram of the

proposed CAD system. Firstly, a skin lesion imag e is

manually segmented . Sec ondly, the propo sed system

is composed of three main blocks: preprocessing, fe-

ature modeling, and classiﬁcation. In the preproces-

sing step, a m edian ﬁlter (Chang et al., 2008) is ap-

plied. Brieﬂy, the median ﬁlter is a nonlinear digital

ﬁlter which is commonly adopted for noise re moval.

It is generally considered in digital image processing

because under certain conditions, it preserves relevant

image information while removing noise. Such noise

decreasing is a typical preprocessing step to improve

the results of subsequent steps. The second step con-

sisting on a feature modeling aims at ﬁlling the gap

between the low-level description an d the diagnosis

concept described b y the BoWs. To cope with the in-

VISAPP 2019 - 14th International Conference on Computer Vision Theory and Applications

formation amb iguity aspect in low-leve l descriptors,

fuzzy modeling ﬁts better our context. Firstly, image

is analyzed for feature extraction in order to deﬁne

our BoW related to four speciﬁc criteria which are:

asymmetry, border, color, and differential structures.

Then, given these features, FCM is applied to deﬁne

the membership degrees of lesion imag e to each word

(concept) of the BoW. For classiﬁcation, DNNs are

adopted since they have been prowed to be the most

efﬁcient in modeling all complex dec isio n making sy-

stems. These main steps are discussed in de ta il in the

following sections.

Median Filter

Feature Extraction

Bag-of-Words

Feature Modeling

Modeling

Data Fuzziﬁcation

DNN Classiﬁer

Decision

Segmented lesion image

Figure 1: Graph representation of main steps of our CAD

system f or decision making about severity of the skin lesion.

4 FEATURE MODELING

4.1 Bag-of-Words Modeling

Dermatologists adopt some concepts to describe ima-

ging sig ns related to melan oma. In our work, the

BoW modeling is inspired by expert interpretation

and diagnosis of melanoma. These concepts help de r-

matologists to identify the features from skin lesion

images which are not visible to the naked eye. Besi-

des, the feature modeling step is performed by FCM

method in ord er to model the fuzziﬁed BoW from ex-

tracted featu res (Abbes and Sellami, 2016; Abb es and

Sellami, 2017).

For asymmetry, dermatologists use different mo-

deled words to describe this concept. We can de-

ﬁne these words by: ”Poor Asymmetry”, ”Medium

Asymmetry” , and ”Highly Asymmetry”. Relatin g to

the borde r, experts model the irregularity of the le-

sion border using different labels. We can describe

these labels by: ”Regular Border”, ”Fine Irr egu la ri-

ties”, and ”Coarse Irregularities”. For the color, the

description of this conc ept is based on the complex-

ity of the color distribution. In our work, two mo-

deled words are deﬁned for the color concept which

are: ”Simple Color” and ”Complex Color”. Based

on the modeled words adopted by the dermatologists,

the differential structure concept is described by two

modeled words wh ic h are: ”Uniform Structure” and

”Composite Structure”. Table.1 deﬁnes the descrip-

tion of each BoW.

4.2 High-level Descriptors

Inspired by the ABCD rule (Stolz, 1994), a feature set

of the skin lesion im age is extracte d for each criterion

(Asymmetry, Border, Color, and Differential structu-

res) in order to determine the lesion severity.

4.2.1 Asymmetry

Dermatologists consider the lesion a symmetry, accor-

ding to the pr incipal axes, as an important descrip-

tor. Indeed, they try to char a cterize the shape and co-

lor asymmetry of a skin lesion, since melanoma cases

tend to be asymmetrically pigmented. While, the be-

nign lesion cases have homogeneous color d istributi-

ons. Moreover, melanoma cases have an asymmetri-

cal shape, whereas, the benign lesions are elliptically

shaped. Therefore, the aim o f asymmetry features is

to deﬁne a quantitative description of the shape a nd

the color of the skin lesion. The asymmetry features

are:

• Central Shape Asymmetry Feature: F

CSA

: This

feature de scribes the degree of lesion asymmetry

relative to the center of gravity. It is computed

in this work by the following method: The seg-

mented lesion is divided by n lines passed by its

center of gravity in order to achieve 2 ∗n equal an-

gles. For each sample, the average length of rays

Deep Neural Network for Fuzzy Automatic Melanoma Diagnosis

Table 1: Description of the bag-of-words.

Concept Bag-of-Words Description

Asymmetry Poor Asymmetry It describes the poor asymmetry of the lesion

Medium Asymmetry It describes the medium asymmetry of the lesion

Highly Asymmetry It describes the high asymmetry of the lesion

Border Regular Border It describes the elliptical border of the lesion

Fine Irregularities It describes the border of t he lesion that contains ﬁne irregularities

Coarse Irregularities It describes the border of t he lesion that contains coarse irregularities

Color Simple Color It describes the lesion that contains at most two colors

Complex Color It describes the lesion that contains at least three colors

Differential S tructures Uniform S tructure It describes the uniform texture of the lesion

Composite Structure It describes the composite texture of the lesion

of each ang le is calculated as it is shown in Figure.

Figure 2: Lesion crossed by n lines(White), lesion border

(Green), centre of gravity (White square), average rays of

each angle of lesion (Red).

Then, the central shape asymmetry feature is de-

termined by chi-square distance of two opposite

average rays as it is expressed in the following

equation:

CSA

∑

i=1

(¯r

(i+n)

− ¯r

)

¯r

(i+n)

+ ¯r

(1)

where ¯r

represents th e ave rage length of the rays

of angle i. High values of this feature describe an

asymmetric lesion, whereas, lower values deﬁne

an elliptical shape.

• Axial Shape Asymmetry: F

ASA

: This fea ture de-

termines the lowest asymmetry of the skin lesion

with respect to an axis passing by the center of

gravity. The chi-squ a re distance of the averages

of two symmetric rays is calcu la ted for each axis.

The axial shape asymmetry f eature is the mini-

mum value that veriﬁes the low est asymmetry as

it is shown in the following equation.

ASA

= min

0≤ j≤(n− 1)

(

(n+ j)

∑

i=(1+ j)

(¯r

(2(n+ j)−i)

− ¯r

)

¯r

(2(n+ j)−i)

+ ¯r

) (2)

High values of this feature are achieved with an

asymmetric lesion.

• Color Asymmetry: F

: This feature evaluates

the symm etrical distribution of colors in a skin

lesion. This color feature is based on th e HSV

color space (Smith, 1978). The skin lesion is di-

vided into two sides where an a xis of separation

is the major axis of the lesion. Each side has a

set of vectors speciﬁc to each space V

i j

with c∈

{Hue, Saturation,Value} : S1 contains V

,...,V

1 j

,...,V

and S2 contains V

,...,V

2 j

,...,V

. Using

the segmented lesion, the color distributions on

each side are compare d usin g chi- square distance.

Therefore, the color asymm etry feature is deter-

mined by the following equation:

∑

c∈{H,S,V }

∑

k=1

1 j

(k) −V

2 j

(k))

1 j

(k) +V

2 j

(k)

(3)

4.2.2 Border

The lesion border c an be determined by the gray le-

vel transition between the inner lesion and the region

surrounding it. Experts try to identify the irregu la r

borders of the lesion, since melanoma cases generally

tend to have highly irregular borders, while a benign

skin lesion has a smooth border. To iden tify this irre-

gularity, we characterize the following features:

• Border Irregularity based on Area: F

: Me -

lanoma cases frequently have irregular localized

patterns. To evaluate these irregularities, a smoot-

hed method is applied to the border o f the lesion.

In fact, the average rays, computed in the previous

section, are marked in the median of each angle.

Therefore, the smoothed border is determined by

the link between all extreme points of the average

rays as it is illustrated in Figure.3.

VISAPP 2019 - 14th International Conference on Computer Vision Theory and Applications

Then, border irregularity feature based on the area

can evaluate the ﬁne irregularities based on the

normalized area between the original shape and

the smoothed shape. A regular bo rder presents

very little d ifference, wherea s, a highly irregular

border has large area differentials.

• Border Irregularity based on the Perimeter:

: This featur e evaluates the coarse irregulari-

ties of the skin lesion. It is deﬁned by the dif-

ference of the smoothed shape perimeter and the

original shape perimeter. A hig h value of this fea-

ture p resents a highly irregular border.

(a)

(b)

Figure 3: Smoothed lesion, original shape (Green), smoot-

hed shape (Red). (a) melanoma l esion where F

=13.64 and

=0.22 (b) benign lesion where F

=5.21 and F

=0.04.

• Shape Signature: F

: T his fe ature is deﬁned by

the variance of the distance of boundary points

from the cen te r of gravity of the skin lesion. Then,

it is normalized to make it scale-invariant, by di-

viding it by the maximum ray which is the max-

imum distance between the center of gravity and

the border points. Finally, this feature is calcula-

ted by the sum of the difference betwee n th e nor-

malized signature and its average.

• Compactness: F

: This feature is deﬁned as

the ratio of the area to the lesion perimeter as it

is shown in the Equation. 4. It is used to evaluate

the connectedness of the skin lesion. Melanoma

cases have low compactness values, wherea s,

benign lesions p resent high ly compa c tness values.

4π.A

(4)

where A is the lesion area and P presents the peri-

meter of the le sio n shape.

4.2.3 Color

The scientiﬁc deﬁnition of melanoma is usually a s-

sociated with the pr esence of diff erent colors, which

can be deﬁned as non-uniform distributions of colors

compare d to the benign skin lesion. De rmatologists

deal with a color numbe r that is between one and six.

Therefore, this color feature evaluates the number of

colors in a skin lesion. A way to identify this fea-

ture in our study is to apply k-means method where

k is the number of colors. The number o f classes ”k”

is varied from one to six. T he result of the k-means

method is a reconstruction of the lesion image a ccor-

dingly. For each reconstruction, the intra- class and

inter-class variance are calculated. The best recon-

struction is achieved with the lowest ratio of intra-

class to inter-class variance. Therefore , it is associ-

ated with the optimal color c la ss number.

4.2.4 Differential Structure

Texture analy sis identiﬁes the region characterization

in an image by their textur e data. It presents one of

the useful methods for the d iscrimination of skin can-

cer with hig h precision . It consists on computing the

statistics of pairs of neighboring pixels, using th e co-

occurre nce matrix (Haralick et al., 1973). The Gray

Level Co-occurrence M a trix ( GLCM) is a relevant ap-

proach f or texture description by mapping the grey

level co-occur rence probabilities based on spatial re-

lations of pixels in different orientations ( Sheha et al.,

2012). The differential structure features are extrac te d

based on texture analysis using normalized GLCM

with an angle value of 0

◦

and a distance value of 1.

To normalize the GLCM, we compu te the sum of all

the values in each GLCM in the array and divide e ach

element by its sum.

The features based on GLCM in our work are as fol-

lows: Correlation, Autocorrelation, Cluster Promi-

nence, Contra st, Cluster Shade , Difference variance,

Dissimilarity, Difference entropy, Entropy, Energy,

Maximum probability, Sum average, Sum entropy,

Sum of squares Varia nce, Sum variance, Homoge-

neity, Inverse difference homog enous (IN V ), Inverse

difference moment nor malized, Inverse difference

normalized (INN), Information measure of correla-

tion 1, and Information measure of correlation 2.

4.3 Modeling using Fuzzy C-Means

4.3.1 Fuzzy C-Means

FCM was determined by Dunn in 1 973 (Dunn, 1973)

and developed by Bezdek in 1981 (Peizhuang, 1983).

In our work, FCM is adop te d for lesion annotation in

order to c luster the lesions with respect to concepts of

each criterion (Asymmetry, border, color, differential

structures), given the features extracted from the skin

lesion ima ge. Data partitioning into clusters is perfor-

med by minimiz ing an objective function. The latter

allows minimizing intra -cluster variance. Minimizing

objective function means increasing similarity among

all the components within a cluster. Therefore, the

Deep Neural Network for Fuzzy Automatic Melanoma Diagnosis

adopted objective function is exp ressed by the fo llo-

wing equation:

J =

∑

i=1

∑

j=1

i j

− c

(5)

where:

• x

is the i

sample of a d-dimensional measured

data;

• c

is the d-dimension center of the cluster j;

• u

i j

is the degree of membership of x

to cluster j;

• k ∗ k is a norm function expressing the Euclidean

distance;

• N is the number of samples;

• C is the number of clusters.

FCM is carried out by an iterative optimizatio n of the

adopted objective function J, applying the update of

the cente rs of clusters c

and the mem bership degrees

i j

. Firstly, the matrix u

i j

is randomly initialized and

then it is m odiﬁed in order to achieve

∑

j=1

i j

= 1. Af-

ter that, the update of the cluster centers c

and the

membersh ip u

i j

is perfo rmed through the following

expressions deduc e d from equation .5:

∑

i=1

i j

× x

∑

i=1

i j

(6)

i j

− c

∑

k=1



−c



(7)

By iteratively updating the cluster centers and the

membersh ip degrees for each sample, FCM modi-

ﬁes the centers of clusters to the ”perfect” location

within a data set. This update iteratio n will stop when

max

i, j

{|u

(k+1)

i j

−u

(k)

i j

|} < ε wh ere k presen ts the iteration

steps, and ε, deﬁning a termination criterion, must be

very low.

4.3.2 Modeling Step

Once the features are extracted from a lesion image,

the different words are modeled. Firstly, as a prepro-

cessing step, we normalize the extracted data. Data

normalization is useful because our data has varying

scales and we use the algorithm that does not make

assumptions about the distribution of extracted data.

Normalization is the process of rescaling one or mo re

features to the interval [0,1]. Accordingly, for each fe-

ature dimension, we calculate the mean of the feature

and subtract it from the d a ta set. After that, we com-

pute the standard deviation of each feature and divide

each feature by its standard deviation as it is shown in

the fo llowing equation:

f n =

| f −

f |

(8)

where:

• f n presents the normalized feature,

• f is the extracted feature,

•

f deﬁnes the mean of the feature f ,

• σ

presents the standard deviations of the feature

f .

For asymmetr y, FCM was carried out to classify

the skin lesions into three classes: ”Poor asymme-

try”, ”Medium asymmetry” and ”Highly asymmetry”,

using the n ormalized features (central shape asymme-

try, axial shape asymmetry, and color asy mmetry).

Accordingly, the membership degrees of the lesion

are computed for each cluster.

Using the normalize d feature of the bord e r, FCM

was applied to model the lesions by the thr ee bag-

of- words: ”Regular border”, ”Fine irregularities” or

”Coarse irregularities”. Thus, the membership de-

grees of the lesio n are calculated f or each word.

Based on the color feature, FCM was performed

to annotate the lesion with two bag-of-words: ”Sim-

ple color” and ”Complex color”. Therefore, the mem-

bership degrees for each modeled feature are compu-

ted.

Based on the n ormalized GLCM features, FCM

was performed to cluster the lesions into ”Uniform

structure” and ”Composite structure”. Then, mem-

bership degrees are deduc ed.

Figure. 4 illustrates the modeling step of each

word of th e BoW.

where:

• NF

CSA

, NF

ASA

, and NF

present the norma lize d

asymmetry features of F

CSA

, F

ASA

, and F

, re-

spectively.

• NF

, NF

, and NF

are the n ormalized

border featu res of F

, F

, and F

, respecti-

vely.

• NCF presents the normalized co lor feature.

• NGLCM deﬁnes the normalized GLCM features.

5 CLASSIFICATION

In medical diagnosis areas, Artiﬁcial Intelligence (AI)

techniques are the most able to cope with such com-

VISAPP 2019 - 14th International Conference on Computer Vision Theory and Applications

CSA

ASA

Fuzzy

C-Means

Poor asymmetry

Medium asymmetry

Highly asymmetry

Fuzzy

C-Means

Regular border

Fine irregularities

Coarse irregularities

Number

Fuzzy

C-Means

Complexe color

Simple color

GLCM

Fuzzy

C-Means

Composite structure

Uniform structure

ofcolors

features

(a)

(b)

(c)

(d)

CSA

ASA

NCF

NGLCM

Normalization

Figure 4: The modeling step from the extracted features.

(a) Asymmetric modeling (b) Border modeling (c) Color

modeling (d) Differential structure modeling.

plexity in decision making. Recently, Deep Neural

Network (DNN) has developed as an active research

sub-area of AI and it has been signiﬁcantly increa-

sed performance on various pattern-re c ognition tasks.

CAD system based on DNN has been reported to be

an ac curate tool for the evaluation of pigmented skin

lesions (Binder et al., 1994; Piccolo et al., 2002; Blum

et al., 200 4). DNNs are considered as an efﬁcient

classiﬁer. It is a computational paradigm based on

mathematical models that unlike traditional compu-

ting have a structure and operation that imitates the

mammal brain (Sordo, 2002). The input features of

DNNs are processed in parallel in interconnected pro-

cessors. Moreover, the ability of adaptation by lear-

ning algorithms adjusts the connectivity betwee n the

nodes of DNNs. DNNs can be trained by speciﬁc

domain samples, therefore, they obtain their ”know-

ledge” abo ut appropriate processing in order to ex-

tract pertinent infor mation from tho se samples.

In our work, we apply the DNN process in order to

make the decision about the severity of the lesion.

Indeed , an input layer of nodes represents external

data which are the membership degrees of the lesion

to each BoW, wher eas a n output layer r epresents the

identity of classes which are the benign and the me-

lanoma classes, and two layers of hidden nodes were

included to represent possible interactions among the

input variables as it is shown in Figure.5. We ap-

ply the hyp erbolic tangent sigmoid as a non-linearity

function and gradient descent as an optimization al-

gorithm in this neural networks.

Hidden

Output

Membership degrees to the BoW

Melanoma

Benign

Layer

Input Layer

Hidden

Layer

Figure 5: A multilayered deep neural network for decision

making based on BoW memberships.

6 EXPERIMENTAL RESULTS

In this section, we show an experimental evaluation of

the proposed CAD system. Firstly, we represent the

experimental data set. Then, we deﬁne the evaluation

metrics as well as the performanc e eva luation of the

proposed CAD system. Based on performance me-

trics, results a re compare d to the existing approa ches.

6.1 Experimental Dataset

In order to validate our work, we use 206 images of

skin lesion, in which 119 are me la noma and 87 are

benign lesions. These skin lesion images were obtai-

ned using stand ard cameras in varying environmental

conditions. Each image has a single region of intere st.

This dataset was extrac te d from the two online public

databases DermQuest (Der, 2012) and Dermatology

Information Sy stem(DIS, 2012). Figure. 6 shows

some samples of our experimental dataset.

Figure 6: Samples of skin lesion i mages acquired by an op-

tical camera: (a) Melanoma cases and (b) Benign lesions.

Deep Neural Network for Fuzzy Automatic Melanoma Diagnosis

6.2 Evaluation Metrics

To evaluate the error rate of the diagnostic results,

some kind of performance metrics is determined in

terms of the true and false negative s as well as true

and false positives. Using these terms, the perfor-

mance of the classiﬁcation is described by the sensiti-

vity, speciﬁcity an d accuracy metrics. Speciﬁcity de-

termines the proportion of negatives tha t are correctly

identiﬁed. In our work, it presents the percentage of

benign lesions which are correctly identiﬁed as be-

nign. Sensitivity deﬁnes the proportion of positives

that are correctly identiﬁed. Here, it presents the per-

centage of m elanomas which are correctly identiﬁed

as melanoma. Whereas acc uracy determines the pro-

portion of negatives and positives that are correctly

identiﬁed to the total nu mber o f benign lesions and

melanoma s. Thus, the perfect diagnostic test would

correctly classify skin lesions with 100% of sensiti-

vity, of speciﬁcity and of accur acy. T hese evaluation

metrics are expressed in the following equations:

Sen sitivity =

T P

T P + FN

× 100 (9)

Speci f icity =

T N

T N + FP

× 100 (10)

Accuracy =

T P + T N

T P + T N + FP + FN

× 100 (11)

where T P is the numb er of true positives, FP is

the number of false positives, FN is the numb er of

false negatives and TN is the number of true nega-

tives. Therefore, TP represents a correct malignant

prediction, FP is the incorr e ct malignant prediction,

FN deﬁnes the incorrect benign predictio n and T N

describes the correct benign prediction. Thus, FP and

FN should be minimized using the proposed CAD sy-

stem.

6.3 Performance Evaluation

The proposed CAD system allows decreasing FP va-

lue, which presents the most dangerous cases. Sen-

sitivity, speciﬁcity, a nd accuracy are the evaluation

metrics of our CAD system. They are calculated in

order to evaluate the diagnostic performance. Accor-

dingly, our system yields a good sensitivity of 90.1%,

an acceptable speciﬁcity of 84.4% and a good accu-

racy of 87.5% on our database.

6.4 Comparative Study

In this sectio n, we d e ﬁne a comparative study of our

approa c h with ﬁve recent studies from the state-of-

the-art (Celebi and Zornberg, 2014; Codella et al.,

2017; L opez et al., 2017; Yu et al., 2017 ; Quang et al.,

2017). We are intere sted in comparing the results of

these different diagnostic approa ches to situate our

approa c h among the state of the art methods. These

previous works are considering dermoscopy images.

Each work d evelops autom ated systems of image ana-

lysis in order to detec t malignant melanoma from skin

lesion images.

About the work of (Celebi and Zornberg, 2014),

authors adopt a k-means clustering algorithm and a

symbolic regression fo r classiﬁcation. T he classiﬁca-

tion rates are nearly 75.8% for speciﬁcity, 7 1.7% for

accuracy, and 61.6% for sensitivity. In (Codella et al.,

2017), authors combin e feature extractors coded by

hand, sparse-coding approac hes, and Support Vector

Machines (SVM), with deep residual n etworks and

fully convolutional neural networks for decision ma-

king about melanoma. This work yields an accuracy

of 85%, speciﬁcity of 93% and sensitivity of 5 4%.

The work of Lopez et al. (Lopez et a l., 2017) is based

on an existing CNN architecture. Firstly, it trains the

CNN from scratch. Then, it uses the transfer learning

method to leverage features from a VGGNet model

pre-train ed on a larger data set. Finally, it keeps the

transfer learning method and ﬁne-tuning the existing

CNN architecture. The classiﬁcation rates are 81%

for accuracy and 78% for sensitivity. In (Yu et al.,

2017), Yu et al. deve lop deep networks, which have

more than 50 layers, for both segmentation and clas-

siﬁcation steps to acquire more relevant features for

more precise recognition. They use the residual le-

arning methods. Indeed, they develop a fully convo-

lutional residual n etwork (FCRN) usin g a multi-scale

contextual inform a tion integration scheme. The eva-

luation of this work yields a sensitivity of 54%, a spe-

ciﬁcity of 93% and an accuracy of 8 5%. Quang et

al. (Quang et al., 2017) use two different CNN ar-

chitectures to improve classiﬁcation accuracy. The

ﬁrst method adopts a simple CNN and it trains the

dataset from scratch. In the seco nd method, they are

based on VGG-16 with ﬁne-tuning approaches in or-

der to make use of pre-trained VGG-16 on ImageNet

dataset. The classiﬁcation rates of the ﬁrst method

are 49% for accuracy, 83% for sensitivity an d 41%

for speciﬁcity. The second method achieves 79% for

accuracy, 34% for sensitivity and 90% for speciﬁcity.

We illustrate the perform ance metrics ( Spec iﬁ-

city, sensitivity, and accuracy) of the re sults of these

studies in Table 2. We note that m ost of the previous

studies are poor in term of sensitivity. Poor sensiti-

vity is due to high false negative r ates, i.e. the rate of

melanoma detection a s a benign lesion is high. Furt-

hermore, we remark that the previous studies based

on CNN architecture are working on unbalanced da-

VISAPP 2019 - 14th International Conference on Computer Vision Theory and Applications

Table 2: Comparative Study.

Performance Sensitivity (%) Sp eciﬁcity (%) Accuracy (%)

(Celebi and Zornberg, 2014) 61.6 75.8 71.7

(Codella et al., 2017) 54 93 85

(Lopez et al., 2017) 78 - 81

(Yu e t al., 2017) 51 94 85

(Quang et al., 2017) Method 1 83 41 49

Method 2 34 90 79

Our work 90.1 84.4 87.5

taset between two classes ( 80% benign lesion class

and 20% melanoma class). Thus, the lesions are di-

agnosed as benign in most cases, and this leads to

increasing the speciﬁcity result rates. Therefore, we

are trying to improve the sensitivity in our study by

keeping an acceptable result of speciﬁcity. Indeed,

our CAD system achieves a good sensitivity of 90.1%

and an acceptable speciﬁcity of 84.4%. This is due

to the relevant extracted f eatures, the fuzziﬁcation of

feature modeling, and th e deep neur al ne twork ba-

sed classiﬁcation. In conclusion, cla ssiﬁcation using

fuzzy feature modeling exhibits the best sensitivity,

while reaching a better accuracy compared to all pre-

vious studies.

7 CONCLUSIONS

In this work, we propose an approach using fuz z y

feature modeling and DNN in orde r to help derma-

tologists and improve the accuracy o f melanoma di-

agnosis based on standard optical im a ges. Firstly,

we present a framework for modeling the extracted

features using FCM method and proposes BoW for

quantifying skin lesion characteristics for melanoma

detection. This BoW is modeled to describe some

human-observable characteristics. The output of this

feature modeling step is the membership degrees of

the lesion to the different label in BoW. Then, these

membersh ip degrees are applied as input to the DNN

classiﬁer. The proposed approach achieves promising

results in a classiﬁcation problem. It yield s the best

result in term of accuracy (87.5%)and of sen sitivity

(90.1%). However, this approach has some inconve-

niences that can be avoided in the future. In future

work, we will improve the speciﬁcity rates by adop-

ted the convolutional neural networks.

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