Image-based Lesion Classification using Deep Neural Networks

Ákos Hermann, Zoltán Vámossy

2022

Abstract

This research explores the topic of moles in cancer using a machine learning approach, with the aim of designing and implementing a system that can determine whether a mole shows a melanoma-like abnormality based on 2D input photographs, and thus whether further examination by a specialist is required. The target system is built around a general-purpose convolutional network, GoogleNet InceptionV3, which has been retrained for the task using a transfer learning technique. In addition to the system, an automated pre- processing phase has been defined to reduce and eliminate anomalies and noise in each sample by means of image processing operations. In conclusion, the system provided 156 correct diagnoses in 180 test cases, indicating a test accuracy of 86.67%, making it an effective melanoma diagnostic tool.

Paper Citation

in Harvard Style

Hermann Á. and Vámossy Z. (2022). Image-based Lesion Classification using Deep Neural Networks. In Proceedings of the 2nd International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE, ISBN 978-989-758-563-0, pages 85-90. DOI: 10.5220/0011126200003209

in Bibtex Style

@conference{improve22,
author={Ákos Hermann and Zoltán Vámossy},
title={Image-based Lesion Classification using Deep Neural Networks},
booktitle={Proceedings of the 2nd International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE,},
year={2022},
pages={85-90},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011126200003209},
isbn={978-989-758-563-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE,
TI - Image-based Lesion Classification using Deep Neural Networks
SN - 978-989-758-563-0
AU - Hermann Á.
AU - Vámossy Z.
PY - 2022
SP - 85
EP - 90
DO - 10.5220/0011126200003209