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 98\%. We also highlight the need for explainability and transparency to ensure human control and safety.
@article{arxiv.2402.00038,
title = {Detecting Brain Tumors through Multimodal Neural Networks},
author = {Antonio Curci and Andrea Esposito},
journal= {arXiv preprint arXiv:2402.00038},
year = {2024}
}
Comments
Presented at NeroPRAI 2024 (co-located with ICPRAM 2024). This version did not undergo peer review: refer to the open access version of record (see DOI)