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18 June 2024 Brain tumor detection using machine learning
Talal Bonny, Maryam Al Jaziri, Mohammad Al-Shabi
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Proceedings Volume 12998, Optics, Photonics, and Digital Technologies for Imaging Applications VIII; 129981V (2024) https://doi.org/10.1117/12.3014635
Event: SPIE Photonics Europe, 2024, Strasbourg, France
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Abstract
Brain tumors represent a critical health challenge, underscoring the urgency of accurate detection for timely intervention. Our study addresses this vital need by employing advanced machine learning techniques. We introduce a novel approach utilizing Convolutional Neural Networks (CNNs) for precise brain tumor classification. Our method involves custom data preparation, a network design and thorough training to improve precision. Additionally, we incorporate the known VGG16 structure into our strategy. Initial findings demonstrate the promise of our algorithm, the VGG16 version in outperforming techniques, for identifying brain tumors. With a remarkable 91.00% accuracy rate for VGG16 - Scenario 1 and a significantly improved 78.33% accuracy for CNN - Scenario 2, our findings highlight the superiority of our CNN-based methodology in achieving higher accuracy. As we continue to refine our approach, we anticipate making significant contributions to the medical field’s ability to accurately diagnose brain tumors.
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Talal Bonny, Maryam Al Jaziri, and Mohammad Al-Shabi "Brain tumor detection using machine learning", Proc. SPIE 12998, Optics, Photonics, and Digital Technologies for Imaging Applications VIII, 129981V (18 June 2024); https://doi.org/10.1117/12.3014635
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KEYWORDS
Tumors
Brain
Cancer detection
Machine learning
Artificial intelligence
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