Detecting Brain Tumors Through Multimodal Neural Networks

Antonio Curci, Andrea Esposito

2024

Abstract

Tumors can manifest in various forms and in different areas of the human body. Brain tumors are specifically hard to diagnose and treat because of the complexity of the organ in which they develop. Detecting them in time can lower the chances of death and facilitate the therapy process for patients. The use of Artificial Intelligence (AI) and, more specifically, deep learning, has the potential to significantly reduce costs in terms of time and resources for the discovery and identification of tumors from images obtained through imaging techniques. This research work aims to assess the performance of a multimodal model for the classification of Magnetic Resonance Imaging (MRI) scans processed as grayscale images. The results are promising, and in line with similar works, as the model reaches an accuracy of around 99%. We also highlight the need for explainability and transparency to ensure human control and safety.

Paper Citation

in Harvard Style

Curci A. and Esposito A. (2024). Detecting Brain Tumors Through Multimodal Neural Networks. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: NeroPRAI; ISBN 978-989-758-684-2, SciTePress, pages 995-1000. DOI: 10.5220/0012608600003654

in Bibtex Style

@conference{neroprai24,
author={Antonio Curci and Andrea Esposito},
title={Detecting Brain Tumors Through Multimodal Neural Networks},
booktitle={Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: NeroPRAI},
year={2024},
pages={995-1000},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012608600003654},
isbn={978-989-758-684-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: NeroPRAI
TI - Detecting Brain Tumors Through Multimodal Neural Networks
SN - 978-989-758-684-2
AU - Curci A.
AU - Esposito A.
PY - 2024
SP - 995
EP - 1000
DO - 10.5220/0012608600003654
PB - SciTePress