Machine Learning and Raspberry PI Cluster for Training and Detecting

Skin Cancer

Elias Rabelo Matos, Edward David Moreno

and Kalil Araujo Bispo

Programa de P

os-Graduac¸

ao em Ci

encia da Computac¸

ao, Universidade Federal de Sergipe (UFS), Sergipe, Brazil

Keywords:

CNN Network, Machine Learning, Skin Cancer, Embedded Systems, High Performance Computing.

Abstract:

Context: Melanoma is the most popular and aggressive type of skin cancer with thousands of cases and deaths

worldwide each year. But melanoma isn’t the only type of skin lesion. Since 2016 the ISIC (International Skin

Cancer Challenge) has been launching challenges toward skin lesion detection. In this paper, we use the

HAM10000 dataset which is part of the ISIC archive and contains seven classes of skin lesions to train a

DenseNet network aiming to achieve state-of-the-art accuracy. Objective: We evaluate the use of a low-cost

cluster with four Raspberry PI to check the viability as a machine learning cluster for detecting one type of

skin cancer. Method: We trained a deep convolutional neural network using the pre-trained model of four

networks and we got 89% of accuracy which is a top state-of-art value. After we perform two experiments:

(i) we use the knowledge transfer technique to run an MLP model using four Raspberry and (ii) we train

the pre-trained DenseNet with a Raspberry PI cluster aiming to verify if a low-cost cluster is viable for this

approach. Results: We found that is not possible to train our network using only four Raspberry PI since it

has low computational resources but we show what more resources are needed to perform this task. Despite

this situation, we achieve 80% accuracy using the knowledge transfer technique and only four Raspberry Pi.

1 INTRODUCTION

Skin cancer is the most common cancer in Brazil.

Data from INCA (Brazilian National Institute of Can-

cer) shows that 180 thousand new cases are de-

tected every year. There are various skin cancer

types, among them the most common is melanoma.

Melanoma has the deadliest form of skin cancer

(American Cancer Society, 2014). Melanoma is a

worldwide danger, in the United States for example,

in 2017, 87 thousand cases were detected with 9730

deaths.

Skin cancer is provoked by abnormal growth cells

of the skin. These cells make layers and the way they

are organized determines the cancer type (sbd, ).

Melanoma is harder and deadliest but it is curable

since detected in its early stages. To detect skin can-

cer in early-stage and make the disease highly curable

dermatologists must be usually consulted for medi-

cal exams. However, in underdeveloped countries like

Brazil or countries without public health care like the

United States, visits to dermatologists not are acces-

https://orcid.org/0000-0002-4786-9243

https://orcid.org/0000-0001-8878-9293

sible to the poorest part of the population.

Dermoscopy is an important exam made by der-

matologists, however, due to the subjectivity of hu-

man diagnosis, then the medical result may differ

among different dermatologist. But, in general, any

dermatologists could examine a set of characteristics

in a skin lesion. These characteristics are called the

ABCDE rule, where: A is asymmetry, B is Border, C

is Colour, D is diameter and E is Elevation/Evolving

(Illig, 1987).

The intrinsic image analysis and pattern recog-

nition involved in skin cancer detection, naturally

makes it an interesting topic for image processing

and artiﬁcial intelligence. The ﬁrst work published in

1994 by Binder et. al(BINDER et al., 1994) already

used dermoscopy images to train a neural network.

In 2016, the ISIC (International Skin Cancer Chal-

lenge) launch an annual competition for melanoma

detection in image analysis. The ISIC archive has

more than twenty thousand images with skin lesion

which are classiﬁed as benign or having a type of skin

cancer, and most part of them is related to melanoma.

In this paper, we use the HAM10000 dataset,

which is a dataset collected from different popula-

tions and is public to use in scientiﬁc experiments.

Matos, E., Moreno, E. and Bispo, K.

Machine Learning and Raspberry PI Cluster for Training and Detecting Skin Cancer.

DOI: 10.5220/0011575500003318

In Proceedings of the 18th International Conference on Web Information Systems and Technologies (WEBIST 2022), pages 75-82

ISBN: 978-989-758-613-2; ISSN: 2184-3252

It was described by (Tschandl et al., 2018) to train

a CNN (Convolutional Neural Network) with ResNet

pre-trained model in Google Colaboratory, and after

achieving the state-of-the-art we try to train the same

algorithm running in two platforms: (i) Google Colab,

and (ii) using an embedded and low-cost cluster, par-

ticularly, composed by Raspberry PI boards. We want

to evaluate the power of low-cost computers toward

skin lesion detection to make dermoscopy accessible

to the poorest people.

To achieve this evaluation we try two experiments:

In the ﬁrst experiment, we performed a ”knowledge

transfer” from a densenet to an MLP running in Rasp-

berry PI. In the second experiment, we try to train a

full dense network on an embedded cluster composed

of only four Raspberry nodes. We found that is not

possible to train our network with only four Raspberry

PI but despite this, we achieve 80% of accuracy using

knowledge transfer.

This paper is organized into six sections as fol-

lows: section 2 presents the related works, section

3 shows the methodology used and the experiment´s

planning, and section 4 shows the experiments made.

Section 5 shows and discusses the results. Finally,

section 6 presents the main conclusions of this re-

search.

2 RELATED WORK

Some papers from the skin cancer detection literature

were useful for our research. In this section, we pre-

sented them with their main contribution and results.

Initially, Abedini et al. (2016) (Abedini et al.,

2016) presented a great contribution to the pre-

processing aspects of skin lesion images. They used

two combined datasets and achieved an accuracy of

94%.

Tajeddin and Asl (2017) (Tajeddin and Asl, 2017)

presented a general algorithm to process image seg-

mentation in skin lesions They used the algorithm

for skin lesion images but claimed that it serves a

general-purpose with different properties and deﬁ-

ciencies. They used a multi-step pre-processing phase

with hair removal, illumination correction, etc. The

proposed algorithm was trained with the ISIC dataset

of 900 images and archived Dice and Jaccard coefﬁ-

cient values of 0.89 and 0.79, respectively.

The work by Majtner et al. (2017) (Majtner et al.,

2017) was among pioneer´s works that used deep

learning for classifying skin lesion images. Their

combined deep learning methods with hand-crafted

Rsuf Features, which is an idea where the feature

set is based on dividing the image into parallel se-

quences of intensity values from the upper-left cor-

ner to the bottom-right corner, and Local Binary pat-

terns achieved 0.826 of accuracy with the sensitivity

of 0.533 and speciﬁcity of 0.898. Their dataset was

compounded by 900 images of benign and malignant

melanoma provided by the ISIC dataset.

The paper by Romero Lopez et al (2017) (Romero

Lopez et al., 2017) presented transfers of knowledge

from a pre-trained CNN method, the VGGNet, to a

dermoscopy approach to a two-class melanoma clas-

siﬁer between malign and benign melanoma. They

used the ISIC dataset and had encouraging results

with 81.33% of accuracy on the test dataset.

Sousa and Moraes (2017) (Sousa and de Moraes,

2017) presented an approach that considers not only

melanoma but Seborrheic Keratosis lesions as well as

their trained different algorithms with GoogleNet and

AlexNet models. Their resized images from dataset to

256x256 in pre-processing phase and user 72 epochs

in GoogleNet 256, 50 epochs in GoogleNet 224, and

30 epochs in AlexNet. Their result of the arithmetic

mean of these three networks was 84.7%.

The main related work is the paper by Sahu et

al. (2018) (Sahu et al., 2018). It used a Rasp-

berry Pi to evaluate the skin lesions analysis in a

hand-held computer, like Raspberry itself, a smart-

phone, or SoCs (System-on-a-chip). The authors pro-

posed and implemented a hybrid approach based on

the use of deep learning models and speciﬁc knowl-

edge domains bases on features that dermatologists

use to detect skin cancer. In the process of obtain-

ing the deep learning features, the authors used a pre-

trained Google MobileNet model. In addition, they

also trained the Raspberry PI and detected that the

training in Movidius Neural Compute Stick is ﬁve

times faster than Raspberry PI. Their ﬁnal task was

to transfer the knowledge to an SVM and run it in a

Raspberry. They used the ISIC 2017 challenge dataset

with 2700 images and achieved a result among the top

10 challengers.

3 METHODOLOGY AND

PLANNING

3.1 Methodology

The main objective of this work is to evaluate the pos-

sibility to build a low-cost cluster of Raspberry PI to

train a network capable of detecting skin cancer. The

ﬁrst step of work is to search the literature in this re-

search area and know the state-of-art in skin lesion

detection. After we plan and describe our experiment,

ﬁrstly training a network on Google Colab. With this

WEBIST 2022 - 18th International Conference on Web Information Systems and Technologies

trained network, we train an MLP algorithm using the

teacher-student method. Immediately after we try to

train our fully dense network in a raspberry PI cluster.

The results are shown in section 5.

3.2 Planning

Context Selection: We select the HAM1000 dataset

in the ﬁrst step while we research the literature. We

have selected this dataset because it is a simple and

already validated data set for skin lesion tasks. Af-

ter, we trained our model to validate the results of our

network using this dataset.

3.3 Hypothesis Formulation

We formulate two hypotheses, one for each experi-

ment that we will perform.

3.3.1 Hypothesis 1

Hypothesis: When we perform a knowledge transfer

from our pre-trained network we lose some accuracy.

Independent Variable: None. Dependent Variable:

Accuracy.

3.3.2 Hypothesis 2

Hypothesis: We think that with the same network

trained in two different environments, the accuracy

will stay the same, but the time spent in training will

be different. Independent Variable: Accuracy. De-

pendent Variable: Time of training.

Instrumentation: The instrumentation process is

to use the Google Colab and the Raspberry PI cluster

conﬁguration. We used the same instrumentation for

both experiments.

4 ROADMAP OF THE

EXPERIMENTS

In this section, we present how we deﬁned, imple-

mented, and ran experiments for this work. We use

the HAM1000 dataset to train a pre-trained DenseNet

using the Pytorch library.

4.1 The Dataset

The dataset chosen to run our experiment was the

HAM1000 dataset described by (Tschandl et al.,

2018). The dataset is called Human ‘Against Machine

with 10000 Images‘, but in fact, this dataset contains

10015 images. These images were collected from dif-

ferent populations and nowadays they are public and

available via the ISIC archive. This dataset is used for

comparisons among machines and human experts and

more than 50% of the lesions are conﬁrmed by pathol-

ogy. This dataset includes seven unbalanced classes

of different types of skin cancer, in Figure 1 one of

each type is shown. They are:

Figure 1: Skin lesion types.

1. Actinic Keratoses

2. Basal cell carcinoma

3. Benign keratosis

4. Dermatoﬁbrom

5. Melanocytic neviare

6. Melanoma

7. Vascular skin lesions

4.2 The Densenet

The Densenet was presented by (Huang et al., 2017)

and is a convolutional neural network (CNN) deeper,

more accurate, and efﬁcient if the connection be-

tween layers of input and outputs is closer. The au-

thors of Densenet say that it has the following ad-

vantages: they alleviate the vanishing-gradient prob-

lem, strengthen feature propagation, encourage fea-

ture reuse, and substantially reduce the number of pa-

rameters. The efﬁciency of Densenet is measured by

achieving the state-of-art in CIFAR-10, CIFAR-100,

SVHN, and ImageNet datasets. In this paper, we used

the DenseNet model provided by the Pytorch library.

4.3 The Resnet

The ResNet network is the winner of the ILSVRC

2015 image classiﬁcation, detection, and location, in

addition to having also won the MS COCO 2015 in

detection and replacement (). As ResNet networks

can be very deep, reaching up to 152 layers learning

Machine Learning and Raspberry PI Cluster for Training and Detecting Skin Cancer

residual representation functions instead of direct sig-

nal representation. The ResNet network introduces

the skip connection concept to circumvent the Vanish-

ing / Exploding Gradient problems Common convo-

lutional networks usually have completely connected

convolutional layers without any skipoushotcurt, thus

being called ﬂat networks. When these networks get

deep the problem of gradient degradation/explosion.

That is: during the backpropagation phase when a

partial derivative has an error in the current weight

this effect is propagated n times, where n is the num-

ber of layers. When a network is deep a small error

multiplied by n will become close to zero (disappear),

while a large error will cause this error to grow (ex-

ploded). problem adding the input of a layer x directly

to the output of a layer ahead. In this way even if the

wandering / exploding gradient is mitigated by x com-

ing directly from some previous layers

4.4 The VGG

The VGG network is an evolution of AlexNet, the

winning network of ILSVRC 2012. It is focused on

smaller entries and larger steps in the ﬁrst convolu-

tional layer and has a greater focus on network depth

than previous convolutional networks (). The VGG

input uses a 224x224 pixel RGB channel, each con-

volutional layer has a very short 3x3 ﬁlter and also

a 1x1 ﬁlter that works like a linear transform. VGG

has a fully connected layer train where the ﬁrst two

have 4096 channels and the last one has 1000 chan-

nels. All of its hidden layers use a ReLU and do

not use local response normalization. The main in-

novations regarding AlexNet are the smaller 3x3 en-

tries compared to 11x11 entries regarding the use of

3 ReLU units instead of just one, the function of de-

cision manages to be more discriminative. With this,

the VGG needs fewer parameters to work. The use

of 1x1 ﬁlter on the layers makes the decision function

more non-linear. With the convolution size, less VGG

has a greater number of layers.

4.5 Inception

The inception network is a milestone in the develop-

ment of convolutional networks because before it the

most popular networks just got deeper in hopes of get-

ting more performance. Inception on the other hand

used several tricks to gain performance, both in speed

and accuracy. It was developed sequentially, thus hav-

ing four famous versions (). In the ﬁrst version the

authors found that choosing the ideal size of the con-

volution kernel is a very complicated task because

while a larger kernel is better for identifying infor-

mation distributed globally, a smaller kernel is used

for local information. Another problem is that very

deep networks can cause overﬁtting and just making

the network larger can be computationally very costly.

For this, the authors decided to use multiple ﬁl-

ters in the same layer, so three convolutional opera-

tions are performed on each layer with three different

ﬁlters, 1x1, 3x3, and 5x5. After the ﬁlters, a max

pooling operation is performed and the concatenated

result is sent to the next layer. The 1x1 ﬁlter is used

to decrease the number of channels, thus making con-

volutions less costly. Versions 2 and 3 were published

in the same work and propose some updates that im-

prove inception performance (). In the second ver-

sion, the 5x5 ﬁlter was replaced by two 3x3 convo-

lutions, as it is still less costly than performing a 5x5

convolution. Version 3 introduced and optimized the

(), factorized 7x7 convolutions, the use of BatchNorm

in sorting aids, and the Label Smoothing technique

used to prevent overﬁtting. For version 4 (SZEGEDY

et al., 2017) the authors identiﬁed that some network

modules were much more complex than necessary, to

mitigate this problem and gain performance, the set

of operations performed before each inception block

was altered.

4.6 Pre-processing, Dataset Split and

Run

We download the dataset from Kaggle

and before

running into the Densenet model provided by Pytorch

we run a function to compute the mean and standard

deviation between image sizes of the dataset and use

these values to Pytorch input normalization.

After that, we use the ”CSV” archive that contains

the class of each image and create a [image, label] tu-

ple for each image and its respective class. We divide

the dataset into three splits: (I) 75% to train and 25%

to test, (II) 80% to train and 20% to test, and (III) ﬁ-

nally, 90% to train and 25% to test. On each split, we

divide 20% of the train set for validation.

We ran the Densenet training in Google Colabora-

tory which provides a 15 GB GPU.

4.7 The Knowledge Transfer

As one of our goals is to use a low-cost embedded

platform and therefore with less computational ca-

pacity, we need to have lighter algorithms from a

computational point of view. For this reason, we

need lightweight algorithms. To run a lightweight

https://www.kaggle.com/kmader/skin-cancer-mnist-

ham10000

WEBIST 2022 - 18th International Conference on Web Information Systems and Technologies

algorithm with good accuracy on the Raspberry PI

we need to translate the knowledge achieved by the

pre-trained networks to an MLP model using the

method teacher-student which is based on the distill-

ing knowledge purpose by (Hinton et al., 2015) that

is a very simple way to improve the performance of

almost any machine learning algorithm. Thus, in this

paper we used knowledge transfer concepts, speciﬁ-

cally the teacher-student technique.

This method uses the previous knowledge that

makes predictions using a whole ensemble of models

which is cumbersome and may be too computation-

ally expensive. Then compress the knowledge in an

ensemble into a single model which is much easier to

deploy. This process makes a small model computa-

tionally cheaper than the previous network.

The called teacher-student technique uses a fully

trained DenseNet network as a teacher and the knowl-

edge is passed to an MLP algorithm using the knowl-

edge and making the MLP algorithm achieve more

accuracy in fewer epochs. The technique works as

follows: the highly complex teacher network is ﬁrst

trained separately using the complete dataset. This

step requires high computational performance. Then

a correspondence between the teacher and student

network must be created. In the third step, the data

is passed through the teacher network to get all in-

termediate outputs. Now the outputs from the teacher

network are used to make the correspondence relation

to the student network and backpropagate error in the

network.

4.8 PyTorch

PyTorch is an open framework for machine learning

that is suitable for use in research until use in pro-

duction. Pytorch works on top of Python, making it

possible to run on all operating systems and architec-

tures with Python interpreters, including ARM archi-

tectures. In this paper, we use Pytorch’s predeﬁned

functions to write our experiment code.

4.9 Apache Hadoop

Apache Hadoop is a framework written mainly in Java

that allows the development of distributed process-

ing of a large amount of data in clusters using sim-

ple programming models. The framework is designed

to be scalable from single servers to hundreds of ma-

chines in a cluster, offering local Hadoop processing

and storage.

Although most of the framework is written in Java

there are also parts written in C and Shell Script. In

this way it is possible through Hadoop Streaming, to

write code in several programming languages, includ-

ing Python. The core of Hadoop is mainly made of

four modules, all Hadoop modules were developed

with the fundamental assumption that the hardware

can fail, so Hadoop, via software, can automatically

deal with these failures (Bappalige, 2014).

4.10 The Experiments Environment

The experiments were executed in the google Colab

and a Raspberry PI Cluster.

4.10.1 Google Colab

Google Collaboratory is a free-to-use tool provided

by Google as part of encouraging the use and learning

of both data science and machine learning. The only

requirement for using the tool is a Google account.

Colab allows the user to create a notebook for the

Python language, in that notebook the codes can be

divided into sections called cells, where the result of

the cells can be stored, making it not need to be ex-

ecuted every time it is necessary. This makes it easy

to store parts of machine learning´s process, such as

pre-processing.

In addition to a Python development environment

with all the language tools, it includes the possibility

of installing new libraries. Colab also allows the user

to execute their codes using CPU, GPU, or TPU.

4.10.2 The Raspberry Cluster

The cluster used in this work is formed by a Rasp-

berry PI cluster with four Raspberry PI 2 Model B

with 1GB of RAM memory connected by a gigabit

switch running an Apache Hadoop Cluster.

4.11 The Experiment Architecture

Figure 2 represents, in the diagram, how experiments

were performed. On top, we have two sets of raw data

that represents the HAM1000 dataset.

In the ﬁrst step to be able of using correctly the

dataset, we make a mapping to attach each image pro-

vided by the dataset to metadata that represents the

lesion using the image ”id”. With this, we can work

effectively on the lesions in the next steps. The pre-

processing phase is performed as we described in 4.6.

We execute the training phase in both ways: (1)

using the Google Colab and (2) using our raspberry

Cluster to make the DenseNet. With our DenseNet,

we apply for the Knowledge Transfer and built our

MLP.

Machine Learning and Raspberry PI Cluster for Training and Detecting Skin Cancer

Figure 2: Experiment Architecture.

5 RESULTS AND DISCUSSION

As we said in Section 4 we split the dataset in

three ways to execute our experiments because the

HAM10000 does not have a traditional standard di-

vision between trainset and testset.

On Table 1 we show each split and how many im-

ages were used for training and testing. For each ex-

periment, we split the trainset into 80% for the train-

ing phase and 20% for the validation phase.

For each experiment, we ran the training 10 times

with ten epochs, and in each run, due to the pre-

trained Densenet, we achieved good accuracy in fewer

epochs. We save the best result models for each ex-

periment and archived an accuracy between 82% and

89% for the Densenet train.

Table 1: Dataset split and image quantity for each experi-

ment.

Split Percentagem Images Quantity

Dataset Split Train Test Train Validation Test

I 75 25 6000 1500 2500

II 80 20 6400 1600 2000

III 90 10 7200 1800 1000

Among the pre-trained networks, the network that

did better in all dataset divisions was the DenseNet

network. With an accuracy of 89% in the III division

of the dataset making it the best result obtained by

Table 2: Accuracy on Four Different Pretrained Networks.

Acurracy

Dataset split

Network I II III Network average

DenseNet 83 82 89 84,6

Resnet 82 81 83 82

VGG 76 80 83 79,6

Inception 79 82 82 81

the model. It is possible to identify with the analysis

of the entire table, that for all networks the division

of number 3 of the dataset is always better. While

divisions I and II ended up being worse. However, it is

possible to identify that the average between the three

data divisions is very close for the four networks.

The analysis of this result indicates that there may

be overﬁtting when a training set is used. To solve

this doubt it is necessary to investigate the number of

entries of each class that was in the test set and if there

was greater learning in any of the classes. However,

this problem was not explored in this work, since its

main objective is to work with networks on the Rasp-

berry platform, as it would be an investigation closer

to the part of artiﬁcial intelligence, it was excluded

from this work. work is the use of the Raspberry

PI Platform, such doubt was little explored by this

work. For direct deployment on Raspberry, this work

addresses two solutions, direct training on Raspberry

PI using a Hadoop cluster and the knowledge transfer

model known as the teacher and student method.

After these tests, we can perform our two experi-

ments using the Raspberry PI.

5.1 Experiment 1: The Knowledge

Transfer

With the best model saved for each experiment, we

execute the transfer process using the four pre-trained

networks to MLP models and run each MLP network

in Raspberry. We measure the accuracy for each MLP

network using the same set of training.

5.1.1 Densenet

Table 3 summarises the accuracy for each experiment

in Densenet and MLP networks. We achieve accuracy

between 73 and 80%.

Table 3: Accuracy on Densenet and MLP Networks.

Experiment Accuracy

Network I II II

DenseNet 83 82 89

MPL 74 73 80

WEBIST 2022 - 18th International Conference on Web Information Systems and Technologies

As expected while transferring the knowledge we

lost some accuracy and yet achieved 80% of accuracy

on experiment III which we considered to be a good

result.

5.1.2 Resnet

Table 4 summarises the accuracy for each experiment

in Densenet and MLP networks. We achieve accuracy

between 65 and 72%.

Table 4: Accuracy on Resnet and MLP Networks.

Experiment Accuracy

Network I II II

Resnet 82 81 83

MLP 65 72 71

In this network, we achieved 72% of accuracy but

curiously split II has more accuracy than split III.

5.1.3 VGG

Table 6 summarises the accuracy of each experiment

in VGG and MLP networks. We achieve accuracy

between 71 and 77%.

Table 5: Accuracy on VG and MLP Networks.

Experiment Accuracy

Network I II II

VGG 75 80 83

MLP 71 74 77

5.1.4 Inception

Table 6 summarises the accuracy of each experiment

in Inception and MLP networks. We achieve accuracy

between 71 and 74%.

Table 6: Accuracy on Inception and MLP Networks.

Experiment Accuracy

Network I II II

Inception 79 82 82

MLP 71 72 74

In this network, we achieved 74% of accuracy in

the best case.

5.2 Experiment 2: The Training in Our

Raspberry Cluster

We select the split number III which contains 90%

for the train set and 10% of the test set to perform

the training phase in our Raspberry PI Hadoop cluster.

We know that accuracy stays the same as the split and

the code are the same. We chose the time of train as

a variable to investigate the viability of the low-cost

cluster with four Raspberry PIs.

To have a good comparison we ran the algorithm

12 times in Google Colab, then we exclude the higher

and lower times to calculate the mean of 10 times left.

And we detect that our network spent a mean of 154.5

minutes to achieve 89% of accuracy in ten epochs.

After this phase, we pass to try to train the same

network in our Raspberry PI Hadoop cluster and we

expected to achieve the same accuracy with a slower

time of training. But this reveals not to be the truth,

the Raspberry PI that we build proved to not be capa-

ble of training our network.

And because of this limitation, we shift our focus

and try to understand why our cluster cannot run the

training phase of our network. We ran again the train-

ing phase of the network in Google Colab to measure

the amount of Memory RAM needed to perform this

training processing, and we detected that it need be-

tween 3.5GB and 5GB of RAM.

Since our cluster has 4GB of RAM in each plat-

form, and the Raspbian OS uses about 80MB of RAM

in the better scenario, and to build the Hadoop cluster

in each node using a java virtual machine uses about

254 MB, then we lose 334MB of RAM available in

each node. So, a minimum of 1.33 GB of RAM is

required to build the cluster.

Therefore, we detected that is unviable to use a

machine learning cluster with just four nodes of Rasp-

berry PI, for the training phase in this cluster. Despite

this result, we can use the single embedded cluster

(using only four Raspberries) for detecting skin can-

cer lesions with 80% of accuracy after using knowl-

edge transfer and making the training phase out of this

cluster, and running the MLP models into the embed-

ded cluster.

The initial objectives of this work were: Train a

network on the Raspberry platform using only Py-

torch and/or do training using an embedded clus-

ter using Apache Hadoop. Experiment number two

shows results that meet this objective as it was not

possible to carry out the training with a cluster of only

four nodes. However, this result is not disheartening

for Low-Cost High-Performance Computing, as there

are still chances of these goals being achieved us-

ing horizontal scaling, adding more nodes, or choos-

ing another platform that provides greater computing

power. For example using other platforms, such as

Banana PI, and Odroid, among others.

Machine Learning and Raspberry PI Cluster for Training and Detecting Skin Cancer

6 CONCLUSION

In this paper, we used machine learning techniques

for evaluating and detecting skin lesions. We used the

HAM10000 dataset, which is a dataset collected from

different populations and is public for use in scientiﬁc

experiments. We have trained a CNN (Convolutional

Neural Network) with DenseNet pre-trained model in

Google Colab, and after achieving the state-of-the-art

we trained the same algorithm running on two plat-

forms: (i) Google Colab, and (ii) using an embedded

and low-cost cluster, particularly, composed by Rasp-

berry Pi boards.

Generally, traditional machine learning is expen-

sive in processing cost but it can achieve good results.

We have nowadays plenty of options of supercomput-

ers available and many of them are free like Google

Colab. Thus, our model was initially trained for run-

ning in Google Colab and we achieve 89% of accu-

racy, but we also used transfer knowledge for reduc-

ing the machine learning models and running them

in a low-cost computer as mentioned in (Sahu et al.,

2018).

So, in this paper, we try to evaluate what is pos-

sible and achieve good results with small comput-

ers like Raspberry Pi. We made the experiment

and achieved 80% accuracy using the teacher-student

method as knowledge transfer. This is a good result

for machine learning research, but it is not adequate

as medical precision. Despite this result, we believe

that is possible low-cost clusters or smartphones can

help dermatologists in their diagnoses.

Attempting to run the training using the Apache

Hadoop cluster proved to be meaningless due to tech-

nical limitations presented in a cluster with only

four nodes. On the other hand, the 80% accuracy

achieved with the teacher-student technique is con-

sidered promising, as this rate is state-of-the-art for

an MLP network. high accuracy artiﬁcial intelligence

at low-cost, as it is possible to overcome this limita-

tion with other platforms or with the addition of more

nodes to the cluster.

Therefore, from this work it is possible to propose

future works divided into two fronts: In the ﬁrst of

them, talking about artiﬁcial intelligence, it is possi-

ble to carry out a work using eight, sixteen, or thirty-

two nodes of Raspberry to build a cluster with higher

computational performance, as well as running tests

on other platforms such as Banana PI or Odroid. For

the artiﬁcial intelligence part, it is possible to propose

studies with different pre-trained networks, with dif-

ferent weights and dataset conﬁgurations, it is also

possible to carry out works isolating the accuracy for

each of the existing lesions in the dataset HAM10000.

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