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

Elias Matos, Edward Moreno, Kalil Bispo

2022

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.

Paper Citation

in Harvard Style

Matos E., Moreno E. and Bispo K. (2022). Machine Learning and Raspberry PI Cluster for Training and Detecting Skin Cancer. In Proceedings of the 18th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-758-613-2, pages 75-82. DOI: 10.5220/0011575500003318

in Bibtex Style

@conference{webist22,
author={Elias Matos and Edward Moreno and Kalil Bispo},
title={Machine Learning and Raspberry PI Cluster for Training and Detecting Skin Cancer},
booktitle={Proceedings of the 18th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2022},
pages={75-82},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011575500003318},
isbn={978-989-758-613-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - Machine Learning and Raspberry PI Cluster for Training and Detecting Skin Cancer
SN - 978-989-758-613-2
AU - Matos E.
AU - Moreno E.
AU - Bispo K.
PY - 2022
SP - 75
EP - 82
DO - 10.5220/0011575500003318