Detecting Brain Tumors through Multi-modal AI Models

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.

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Associated paper

  1. Detecting Brain Tumors Through Multimodal Neural Networks

    Workshop International
    • Antonio Curci, Andrea Esposito
    • Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods
    • 10.5220/0012608600003654
    Open access PDF
    Full citation
    Antonio Curci and Andrea Esposito. 2024. Detecting Brain Tumors Through Multimodal Neural Networks. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods, SCITEPRESS - Science and Technology Publications, Rome, Italy, 995–1000. DOI:10.5220/0012608600003654
    Details

Introduction

Brain tumors pose a significant challenge in medical diagnostics due to their intricate nature. Early detection plays a pivotal role in determining patient outcomes. Our study delves into the potential of Artificial Intelligence (AI), particularly deep learning, to aid in the detection of brain tumors from imaging data, with the aim of streamlining the diagnostic process and improving patient care.

The Findings

In our research, we developed and tested an AI model designed to analyze MRI scans for the presence of brain tumors. Through the utilization of multimodal neural networks, our model achieved an impressive accuracy rate of approximately 99%. This indicates promising potential for AI to assist in the detection of brain tumors, offering a valuable tool for healthcare professionals.

Significance

The significance of our findings lies in the potential to enhance diagnostic efficiency and patient outcomes in the field of neurology. By leveraging AI technology, we aim to facilitate earlier detection and intervention for brain tumors, ultimately improving treatment efficacy and patient prognosis. Our study contributes to the ongoing efforts to integrate innovative technologies into medical practice, with the overarching goal of enhancing patient care and outcomes.

Transparency and Oversight

While AI holds promise in medical diagnostics, we emphasize the importance of transparency and human oversight in its implementation. Clear explanations of AI processes and mechanisms, coupled with human control, are essential to ensure patient safety and maintain the integrity of the diagnostic process. Through transparent and accountable AI practices, we strive to uphold ethical standards and promote trust in AI-assisted healthcare.

Conclusion

In conclusion, our research represents a significant step forward in the field of brain tumor detection. By harnessing the power of AI, we aim to improve diagnostic accuracy and efficiency, ultimately benefiting patient care. Moving forward, we remain committed to further exploration and refinement of AI-assisted diagnostics, with a focus on transparency, accountability, and patient-centered care.

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