Tropical Skin Disease Classiﬁcation using Connected Attribute Filters

Fred N. Kiwanuka

, Omar Eltaher Abuelmaatti

, Anang Hudaya Muhamad Amin

and Brian J. Mukwaya

Division of Computer and Information Science, Higher Colleges of Technology, Dubai, U.A.E.

PredictX, Uganda

Keywords:

Max Tree, Connected Operators, Connected Filters, Attribute Filters, Skin Diseases, Deep Learning.

Abstract:

Morphological connected ﬁlters operate on an image through ﬂat zones which comprise the largest connected

components with a constant signal. These ﬁlters identify and ultimately extract the whole connected com-

ponents in an image without alteration of their boundaries and thus shape preserving. This is a desirable

property in many image processing and analysis applications. However, due to the variability of the number

of connected components, even in the case of images of the same resolution and size, their application in

classiﬁcation tasks has been limited. In this study, we propose an approach that computes the shape and size

features of connected components and use these features for the classiﬁcation of bacterial and viral tropical

skin infections. We demonstrate the performance of the approach using gradient boosting machines and com-

pare the results to deep learning approaches. Results show that the performance of our approach is comparable

to that of Convolutional Neural Networks (CNN) based approach when trained on 1460 images. Moreover,

CNN was pre-trained and required augmentation to achieve that perfomance. However, our approach is at

least 56% faster than CNN.

1 INTRODUCTION

Mathematical morphology has contributed a wide

range of operators to image processing and analysis.

A number of efﬁcient algorithms have been developed

for image representation and description. These oper-

ators (Salembier and Serra, 1995; Heijmans, 1999)

have been used in many applications including; medi-

cal image processing (Kiwanuka et al., 2009a), image

segmentation and reconstruction (Salembier, 2015),

object detection and recognition (Urbach, 2015), doc-

ument analysis (Ouzounis and Wilkinson, 2011),

characters recognition (Mennillo et al., 2015) video

processing(Salembier et al., 1998), color process-

ing(van de Gronde and Roerdink, 2014) as well as

remote sensing (Soille, 2008).

For many of these applications, an important task

is to extract particular regions of an image while pre-

serving as much of the contour information as possi-

ble. This is what has made connected ﬁlters (Salem-

bier and Wilkinson, 2009), a strictly edge preserv-

ing class of operators in mathematical morphology,

a popular choice of selection. These operators act by

merging ﬂat zones, given some criteria, and ﬁlter an

image without introducing new contours. They oper-

ate on connected components rather than pixels.

A sub-class of connected ﬁlters are attribute ﬁlters

(Breen and Jones, 1996; Salembier et al., 1998). They

allow ﬁltering based on the properties or features of

connected components in the image. Examples of

attribute ﬁlters include attribute openings, closings,

thickenings, and thinnings (Meijster and Wilkinson,

2002; Breen and Jones, 1996; Salembier et al., 1998)

(Urbach et al., 2007; Westenberg et al., 2007). At-

tribute openings (Breen and Jones, 1996; Salembier

et al., 1998) allow the use of size based features. By

contrast, attribute thinnings allow the use of shape-

based features, which require translation, scale and

rotation of invariant descriptors.

Although connected ﬁlters have been popular in

many image processing and analysis tasks, they have

hardly been used in classiﬁcation tasks. This is be-

cause the majority of image classiﬁcation tasks re-

quire all images to be of a ﬁxed size. However the

number of connected components in images of even

the same pixel size varies depending on a number

of factors including connectivity of the pixels. Con-

nected and attribute ﬁlters extract connected compo-

nents using the notion of connectivity. Connectivity

describes the way pixels are grouped to form con-

nected components or ﬂat zones in a grayscale image.

An example is shown in ﬁgure 1. As can be seen in

338

Kiwanuka, F., Abuelmaatti, O., Amin, A. and Mukwaya, B.

Tropical Skin Disease Classiﬁcation using Connected Attribute Filters.

DOI: 10.5220/0010203403380345

In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP, pages

338-345

ISBN: 978-989-758-488-6

Figure 1: Sample viral infection image (left) with its

grayscale and connected peak components representation at

different threshold levels.

ﬁgure 1, the image contains object like patches and

hence makes conclusive sense to engineer features

with the notion of objects. In this paper, we propose a

classiﬁcation approach based on the features of con-

nected components rather than pixels. We use images

of skin infection to demonstrate our idea.

Skin diseases classiﬁcation has proven to be chal-

lenging for many approaches in the literature, includ-

ing deep learning due to diversity due to color, expo-

sure, scale, level of details, illumination and appear-

ance.

The paper is organized as follows. A brief back-

ground of the skin disease classiﬁcation challenge is

ﬁrst presented in Section 2. In Section 3, the theory

of connected and attribute ﬁlters is described; while

data collection is covered in Section 4. Feature en-

gineering is covered in Section 5. This includes the

attributes description,and the Max-Tree approach. In

Section 6, the performance evaluation of our approach

is presented including a discussion of the results. We

give concluding remarks in Section 7.

2 SKIN DISEASES IN TROPICAL

REGIONS

According to a global dermatology report published

in 2017 (Karimkhani et al., 2017), skin diseases rep-

resent up to 23.7% of the main organ-speciﬁc reasons

for visiting a primary healthcare centre in Sub Sahara

Africa. In Uganda, the cause of death attributed to

skin diseases in 2017 was 0.36% of total deaths. The

cause of a skin infection depends on the type of infec-

tion. The most common skin conditions in Sub Sahara

Africa are attributed to bacterial, viral and fungal in-

fections. According to the World health Organization,

this is attributed mainly to the hot humid climates,

poor living conditions and overcrowding.

Bacterial skin infections often begin as small, red

bumps that slowly increase in size. Some bacterial

infections are mild and easily treated with topical an-

tibiotics, however other infections require an oral an-

tibiotic.There are different types of bacterial skin in-

fections including; cellulitis, impetigo, boils, and lep-

rosy.

Viral skin infections are caused by a virus. These

infections range from mild to severe. Examples of vi-

ral infections include; shingles (herpes zoster), chick-

enpox, Molluscum contagiosum, warts, measles, and

hand, foot, and mouth disease.

Fungal skin infections are caused by a fungus and

are most likely to develop in damp areas of the body,

such as the feet or armpit. Many fungal infections

are typically non-life-threatening and can include ath-

lete’s foot, yeast infection, ringworm, nail fungus,

oral thrush, and diaper rash.

A variety of skin tests may be performed to diag-

nose skin allergies, bacterial, viral, or fungal skin in-

fections. A culture test is a widely used method that is

done to identify the microorganism (bacteria, fungus,

or virus) that cause the infection. Skin (surface scrap-

ings, biopsies, contents of pus bumps and blisters),

hair, or nails may be cultured to detect bacteria, fungi,

or viruses. However, poverty and a very high patients-

to-dermatologist ratio in Sub Saharan African coun-

tries hinders proper diagnosis using culture tests. The

commonest diagnosis in these countries is often, vi-

sual inspection of symptoms based on the appearance

and location. This leads to misdiagnosis and unnec-

essary economic burden since some infections do not

require medication. For example some types of viral

skin infections may improve on their own and heal

within days or weeks.

The boundary between these two cases in many

diagnoses is difﬁcult to be drawn. To illustrate the

nature of this problem, and its difﬁculties, we show

examples of image patches as shown in Fig. 2. In

this research we attempt to automate this boundary

separation using machine learning. We focus on viral

and bacterial as there was no sufﬁcient data for fungal

infection.

2.1 Related Work

A number of studies have looked at image processing

and computer vision methodology for automated di-

agnosis of skin diseases. (Liao et al., 2018), investi-

gated the feasibility of constructing a universal skin

disease diagnosis system using deep Convolutional

Neural Networks (CNN). They trained the CNN ar-

chitecture using 23,000 skin disease images from

the Dermnet dataset and tested its performance on

Tropical Skin Disease Classiﬁcation using Connected Attribute Filters

339

Figure 2: Sample images, viral images at the top and bacte-

rial cases at the bottom.

Dermnet and OLE skin disease open dataset. Another

study by (Sun et al., 2016), introduced a benchmark

multiclass dataset for clinical skin diseases for clas-

siﬁcation. They used 6,584 images from 198 classes

of varying scale, some color, shape and structure and

used CNN for the classiﬁcation. A number of other

studies have proposed other approaches for a review

see (Barati et al., 2011), (Bi et al., 2017), (Romero-

Lopez et al., 2017), (Pomponiu et al., 2016).

Unlike these approaches, our approach is con-

nected component based rather than pixel based.

They also utilized open sourced popular datasets of

dermoscopic images, none of which is from tropical

region based that comes with its own challenges of

disease.

In this research we use real ﬁeld conditions of

skin infection datasets taken using a mobile phone in

Uganda and to the best of our knowledge connected

ﬁlters have not been used in classiﬁcation tasks of this

nature. An attempt to use them was made in (Quinn

et al., 2014).

3 CONNECTED FILTERS

This section brieﬂy reviews the concept of connected

attribute ﬁlters. Let binary images X, Y be subsets

of E, where E is a non-empty universal set with

P (E)being a family of all subsets of E. If the images

are grey-scale then it becomes a mapping from E to

some subset of R or Z. We say a set C ⊆ X is a con-

nected component of X if C is connected, and if there

is any other such that connected set D, C ⊆ D ⊆ X

then C is equal to D (Kiwanuka and Wilkinson, 2015).

3.1 Attribute Filters

An operator ψ (Heijmans, 1999) operating on binary

image X is connected if and only if the set difference

X\ψ(X ) is exclusively composed of the connected

components of X or its complement X

. To extract

connected components from images, we use a family

of operators called connectivity openings. Let Γ

(X)

be the connectivity openings x ∈ E, Γ

(X) returns all

the connected sets within X that have a point x ∈ E in

their intersection. If the union is C, then C ⊆ X, such

that x ∈ C, is the maximal connected set containing x

(Serra, 1998).

Given Γ

(X) of X at a point x ∈ E with C a subset

of P (E), binary connectivity opening are deﬁned in

(Kiwanuka and Wilkinson, 2015) as :

(X) =

(

∈C|x ∈ C

, C

⊆ X } if x ∈ X

0 otherwise.

(1)

C ⊆ P (E) is the family of all connected sets in E.

A class of connectivity openings has the properties

of: (i) anti-extensive since Γ

(X) ⊆ X, (ii) increasing

since X ⊆ Y ⇒ Γ

(X) ⊆ Γ

(Y ) this is the case of size

based ﬁltering criteria. It becomes non-increasing for

shape based and (iii) idempotent since Γ

(Γ

(X)) =

(X).

Once the connected components have been ex-

tracted, then attributes to ﬁlter the connected con-

nected components is then computed. This where

attribute ﬁlters (Breen and Jones, 1996; Salembier

et al., 1998), a family of connectivity openings that

imposes constraints on the connected components

they return comes in. The constraints are expressed

in the form of a binary criteria to make a decision of

whether to accept or to reject components. Breen and

Jones (Breen and Jones, 1996) deﬁne the attribute cri-

terion as:

Λ(C) ≡ Attribute(C) ≥ λ, (2)

with Attribute(C) is the attribute of the connnected

component C and λ the threshold. In our classiﬁcation

task, our interest is this attribute computed here.

After extracting the connected components using

connectivity openings and attribute computed, the ﬁl-

tering is then applied. If ψ

is the ﬁlter and Λ is the

attribute criteria, then ψ

can be deﬁned as

(

C if Λ(C) is true

0 otherwise

(3)

The result of the ﬁltering of the image X is then given

as:

(X) =

[

x∈X

(Γ

(X)) (4)

This represents the union of all connected fore-

ground components which meet criterion Λ. The dual

counterparts of these operators are attribute closings

and thickening respectively and can be deﬁned as

(X) = (ψ

))

(5)

VISAPP 2021 - 16th International Conference on Computer Vision Theory and Applications

340

where X

= E \ X denotes the complement of X. This

removes connected background components which

do not meet the criterion Λ. The attribute could be

for example area of a connected component which is

then compared to Λ which is a predeﬁned threshold

and returning true if the value is above the threshold

and false otherwise. In our approach we do not re-

quire this.

The same principle can be extended to gray scale

images (Breen and Jones, 1996; Salembier and Serra,

1995), where connectivity rely on the notion of par-

titions of ﬂat zones. A partition is deﬁned as the set

of non overlapping, non void regions within the im-

age domain E. More formally, Connected operators

in this case act on connected components of level set

images L

of image f which can be deﬁned as:

( f ) = {x ∈ E| f (x) = h}. (6)

Where h is the gray level of the image. There is

a binarization at every level L

of the image. For

more on connected operators see (Breen and Jones,

1996; Salembier et al., 1998; Urbach et al., 2007),

(Kiwanuka and Wilkinson, 2015).

4 DATA COLLECTION

Through a NUFFIC funded project, skin infected im-

ages were collected from skin clinics in three Ugan-

dan districts of Kampala, Gulu and Mbarara. First, the

patients were clinically examined by dermatologists,

then culture laboratory tests were conducted to ascer-

tain and conﬁrm the skin infection. The dermatolo-

gists then captured more images using a 4MB pixel

resolution phone camera from the patients whose re-

sults were indicative of either a viral or bacterial in-

fection. The images were then preprocessed and in

some cases were split where it was deemed that differ-

ent parts of the images were fairly distinct and would

aid the classiﬁcation. 1824 images (912 viral, 912

bacteria) were captured. Fig. 2 shows examples of

the sample images for bacterial and viral skin dis-

eases. Each image is of size 80x80 pixels.

5 CONNECTED COMPONENTS

FEATURE ENGINEERING

A large number of both size and shape features is

available for connected ﬁltering for 2D and 3D imag-

ing modes (Breen and Jones, 1996; Salembier et al.,

1998; Urbach et al., 2007; Kiwanuka et al., 2009a).

These features enhance the ability of connected ﬁlters

to select structures of interest for different imaging

modalities. However in this research we utilize them

for classiﬁcation. Speciﬁcally, we compute attributes

for 2D imaging modes.

5.1 Size based Features

For size based attributes, we considered:

• Area - area is easily estimated by counting the

number of pixels that constitute a connected com-

ponent.

• Perimeter of a connected component

• Minimum child gray level of peak connected

component

• Current gray level of peak connected component

• Maximum child gray level - current gray level

5.2 Shape based Features

Features are considered as shape descriptors pro-

vided they satisfy three key properties: translation,

scale, and rotation invariance. We considered mo-

ment based attributes like moment of inertia, non-

compactness, elongation, ﬂatness, sparseness, and

jaggedness. For all these attributes and their vari-

ants see (Urbach, 2015), (Kiwanuka et al., 2009a),

(Kiwanuka et al., 2009b), (Kiwanuka and Wilkinson,

2010), (Quinn et al., 2014), (Kiwanuka and Wilkin-

son, 2015). Here brieﬂy is a formulation of some of

them. The moment-of-inertia I(C) of an object can be

deﬁned as its tensor which is equivalent to the covari-

ance matrix multiplied by the number of pixels in a

connected component(C). The compactness attribute

N(C) is deﬁned as

N(C) =

(C))

4πA(C)

(7)

where A is the area of each connected component

and P is the perimeter. Other moment-invariants can

be obtained using geometric interpretation from the

eigenvalues of tensor matrix and are derived as fol-

lows. Let e

(C), e

eigenvalues of I(C) such that:

(C)| ≥ |e

(C)| (8)

The measure of elongation ξ(C) is given by

ξ(C) =

(C)|

(9)

While ﬂatness z(C) is given by:

z(C) =

(C)|

(10)

Tropical Skin Disease Classiﬁcation using Connected Attribute Filters

341

We also considered four non-normalized central

moments and the normalized geometric moments.

For more about these features see (Urbach et al.,

2007).

In total we computed 14 size and shape features

for each connected component.

5.3 Computing Features using the Max

Tree Data Structure

We utilized the Max-Tree data structure (Salembier

et al., 1998) an efﬁcient data structure for gray scale

image processing. In the Max Tree data structure, the

ﬁltering process is made of four stages: the creation of

Max-Tree, feature computation, ﬁltering, and restitu-

tion. In our approach, we only use the ﬁrst two stages.

Many fast algorithms have been developed to

build a Max-Tree like (Salembier et al., 1998; Wilkin-

son, 2011). Like any other tree based data structure,

the Max tree arranges the connected component of an

image into a tree with the root node acting as a par-

ent to all subsequent nodes where for the case of gray

scale images, a node represents a ﬂat zone L

where

a set of pixels adopts a single gray-level value of the

highest node within that subset. Binarization of the

image is then carried out at each level h to obtain the

thresholded set consisting of peak components, P

whose gray-level ≥ h (k is node index). C

are the

components in P

with gray-level h. An example of

the Max tree is shown in Fig. 3. There are two ways

to compute the attributes. One is during the Max Tree

building phase where data needed for computing the

node features is collected. This data can then be used

to compute the features, that describe shape or size of

the peak components represented by the nodes.

Once the features have been computed, then the

ﬁltering process is done to decide what connected

components preserve usually based on certain rules

like the Direct, Min, Max, and Viterbi rules (Breen

and Jones, 1996), or the Subtractive rule (Urbach

et al., 2007). However, in our research, rather than

ﬁlter, we simply stack the features for each image. It

is these features that we use as input for training our

Gradient boosting model. To deal with the variable

number of connected components of the different im-

ages, we ﬁx the number of connected components of

all images to be equal to the worst case of the number

of pixels in the image. In practice, instead of repre-

senting the image with pixel values we represent the

image as a set of connected components. We then

compute the features for those connected components

using the Max-Tree data structure. Suppose, the num-

ber of connected components in an image is n and the

size of the image is MxN, where n ≤ MxN during the

Input image Thresholded sets(peaks) Max-Tree

Figure 3: An example of a Max-Tree representation of a

simple 4-level image.

feature preparation for training, we enforce that n =

MxN by assigning zeros to the extra components cre-

ated.

6 CLASSIFICATION

In this section we describe the training process of two

approaches:

1. Our approach : connected components feature en-

gineering

2. Convolutional Neural Networks (CNN) approach

that trains a model from scratch using our little

data that we have.We then use data augmentation

and ﬁnally use a pre-trained CNN model

6.1 Connected Feature Approach:

Implementation

We computed 14 features for each connected compo-

nent in the image and stacked them. We then nor-

malized each image to have the number of connected

components to be equal to the size of the image MxN.

The extra connected components have features set at

zero. Let n be the actual number of connected com-

ponent and E the number of pixels in the image. The

number of connected components set to zero can be

given as the difference E − n. During the exploratory

analysis we had carried out feature selection to nar-

row down the features with the most informative for

our task.

To establish the performance of our proposed con-

nected attribute ﬁlters approach, we ran experiments

on the skin disease dataset and evaluated the perfor-

mance of these features. In all of the experiments,

we randomly split the datasets into 80 % and 20%

from each class as the training set and the rest as test-

ing set respectively. For the training we used gradi-

ent boosting machines classiﬁer. A gradient boosting

machine, much like a random forest, is a machine-

learning technique based on ensembling weak predic-

tion models, generally decision trees. It uses gradi-

ent boosting, a way to improve any machine-learning

model by iteratively training new models that special-

ize in addressing the weak points of the previous mod-

VISAPP 2021 - 16th International Conference on Computer Vision Theory and Applications

342

els. The choice of gradient boosting is due to its ex-

cellent ability to deal with non perceptual data and

being one of the most commonly used techniques in

Kaggle competitions besides deep learning.

To ensure that we reduce overﬁtting, we employed

regularization methods that penalize various parts of

the algorithm and generally improve the performance

of the algorithm. Experimentally:

• Choose a learning rate of 0.05 after experimenting

with the rates between 0.05 and 0.2.

• The optimum number of trees for this learning

rate was 2435.

• Fixed tree hyperparameters and tune learning rate

and assessed speed and performance.

• Tuned tree-speciﬁc parameters for decided learn-

ing rate.

• Hyperparameter settings was carried and cross

validation procedures to get more robust esti-

mates.

6.2 Convolutional Neural Networks

(CNN)

We ﬁrst trained a CNN model from scratch on our

dataset but we split the data in 60% training, 20%

for validation and 20% testing. For the implementa-

tion of CNN, we stacked alternated Conv2D with relu

activation and MaxPooling2D layers to augment the

capacity of the network and also reduce the size of

the feature maps before the ﬂatten layer. We ended

with a network with a Dense layer of size 1 using

a sigmoid activation. Even with numerous experi-

mentation with various architectures and parameters

tuning, it was apparent that the network was overﬁt-

ting. The training accuracy increased linearly over

time, until it reached nearly 78%, whereas the vali-

dation accuracy stalled at about 56%. The validation

loss reached its minimum after a few epochs and then

stalled, whereas the training loss kept decreasing lin-

early until it reaches near zero.

We then explored data augmentation to deal with

the overﬁtting problem. We trained a new network

using data-augmentation conﬁguration. As such, this

was not enough to completely get rid of overﬁtting.

To further ﬁght overﬁtting, we added a Dropout layer

to our model before the densely connected classiﬁer.

By using regularization and tuning the network’s pa-

rameters including the number of ﬁlters per convolu-

tion layer, and the number of layers in the network,

we reached an accuracy of 68.6%, a 13% relative im-

provement.

It became apparent that it would be difﬁcult to

go any higher just by training your own CNN from

scratch, because of the not sufﬁcient data to work

with. As a next step to improve accuracy on this prob-

lem, we explored using a pretrained model.

We used a pretrained network of ImageNet

(Krizhevsky et al., 2012) a popular computer vision

model trained on a large dataset for large-scale image-

classiﬁcation task with 1.4 million labeled images and

1,000 different classes.

Given that the original dataset is large enough and

general enough, then the spatial hierarchy of features

learned by the pretrained network can effectively act

as a generic model for our skin classiﬁcation prob-

lem even though our problem has completely differ-

ent classes than those of the original task. A num-

ber of skin classiﬁcation (Bi et al., 2017), (Romero-

Lopez et al., 2017), (Pomponiu et al., 2016), studies

have used this pretrained CNN models of ImageNet

reporting very high accuracy of at least 90%. We

used the VGG16 architecture (Simonyan and Zisser-

man, 2015) because of its simplest and wide use. We

considered a pretrained network with feature extrac-

tion.

Using the convolutional base of the VGG16 net-

work, trained on ImageNet, to extract features from

images, and then train virus and bacteria classiﬁer on

top of these features. Due to the skin classes in our

dataset, we changed the original 1000 classes clas-

siﬁcation layer to a binary layer, whose weights are

randomly initialized, then added Dense layers on top,

and run it end to end. We also employed data aug-

mentation, which as seen earlier improved accuracy

earlier and reduced overﬁtting. Before compiling and

training the model, we freeze the convolutional base

thus preventing the weights from being updated dur-

ing training. However this technique is far more com-

putationally expensive.

We were able to achieve an accuracy of about

76.3%. This is much better than that trained from

scratch.

6.3 Evaluation

From the experiments, gradient boosting using our

features from connected components achieved an ac-

curacy of 75.2% as compared to the best convolution

neural networks which achieved 76.3%. The perfor-

mance of the two approaches is relatively low com-

pared to approaches seen in literature for skin classi-

ﬁcation like in (Bi et al., 2017), (Romero-Lopez et al.,

2017), (Pomponiu et al., 2016), this can be explained

by the nature of the dataset. The ﬁeld conditions, the

type of skin and other factors make this dataset diﬁ-

cult to classify. For many images in our dataset as

seen in Fig. 2, viral and bacterial patches tend to

Tropical Skin Disease Classiﬁcation using Connected Attribute Filters

343

Figure 4: ROC curves for Gradient Boosting and CNN.

Figure 5: Confusion Matrix with Bacterial (0) and Viral (1)

infection.

blend into normal skin, making separating the two

classes more difﬁcult as compared to open datasets

like those used in (Bi et al., 2017), (Romero-Lopez

et al., 2017), (Pomponiu et al., 2016). However, our

approach was faster in computing times by at least

56% as seen in Table 1.

The Receiver Operating Characteristic (ROC)

curve is shown in Figure 4. ROC curve for our ap-

proach and CNN showed a similar performance be-

tween the two approaches. The Area Under the Curve

(AUC) of 0.90 and 0.91 indicates that the classiﬁer

performs well.

The confusion matrix in Fig. 5 shows that the gra-

dient boosting classiﬁer performed well on classify-

ing the bacterial infection (0) as compared to the viral

infection (1). This can be attributed to the nature of

bacterial infections having a clear morphological vi-

sual description of bumps as compared to the viral

infections as seen in Fig. 2.

6.4 Computational Timings

Using a standard Core 4 Duo E8400 at 2.0 GHz ma-

chine with 16 GB RAM, we performed timings for

Table 1: Performance Evaluation.

Model Accuracy(%) Computing Algorithms Time(seconds)

Gradient Boosting using Connected Filters 75.1 216

CNN with augmentation 68.0 313

Pre-trained CNN 76.2 338

the computation of our approach and that of CNN. For

the connected attribute ﬁlter approach the timings in-

clude the time to compute the attributes from the max

Tree as well as the training. For the CNN, since the

feature engineering is inherently learned during the

training, the timing include the whole cycle of, deﬁn-

ing the model, compiling the model, and ﬁtting the

model. The performance of the two approaches can

be seen in Table. 1 where it is clear that our approach

was faster in computing times by at least 56%.

7 CONCLUSIONS

In this paper, we proposed an approach for using con-

nected attribute ﬁlters for classifying skin infections.

With 75.1% level of accuracy, our approach is com-

parable to the performance of Convolutional Neural

Networks. We compared the classiﬁcation accuracy

of a CNN trained from scratch, a pretrained CNN

with transfer learning and frozen layers. All three

conﬁgurations were tested with 364 images and the

last-mentioned conﬁguration achieved the highest ac-

curacy of 76.3% Our approach is at least 56% faster

than CNN in computational time. In future, we intend

to investigate the performance of our approach when

combined with deep learning. This should be possi-

ble since connected ﬁlters tend to behave by design

like deep learning models with the ﬂat zones as hid-

den layers as used in deep learning, however without

back propagation.

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