Detecting Brain Tumors Through Multimodal Neural Networks

DOI 10.5220/0012608600003654

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.


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Detecting Brain Tumors through Multi-modal AI Models

March 11, 2024

Brain tumors are tricky to diagnose and treat due to the brain’s complexity. Detecting them quickly improves patient chances. Our study explores using Artificial Intelligence (AI), specifically deep learning, to save time and resources in finding tumors from imaging. We tested an AI model on MRI scans and achieved about 99% accuracy. We also emphasize the importance of explaining and being transparent about how the AI works to ensure human control and safety in the diagnostic process.