Advanced YOLOv8 architecture for multi-class brain tumor detection
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Abstract
Background: Accurate brain tumor detection is critical for early diagnosis and effective treatment planning in neuro-oncology. Magnetic resonance imaging (MRI) is a cornerstone for identifying and localizing brain tumors, guiding clinical interventions, and enhancing patient outcome. Advanced deep learning models, such as YOLOv8, offer promising solutions for automated and precise tumor detection in MRI.
Objectives: This study aimed to develop and evaluate a YOLOv8-based model for the accurate identification and localization of brain tumors, including pituitary tumors, meningiomas, and gliomas, using a publicly available Kaggle dataset of annotated MRI images.
Materials and methods: The YOLOv8 pretrained model was employed for brain tumor detection on a Kaggle dataset comprising annotated MRI images, including cases with pituitary, meningioma, and glioma, and no tumor. The dataset was pre-processed and split into training and validation sets for further analysis. The YOLOv8 model was fine-tuned to optimize the tumor detection and localization. Performance metrics, including precision, recall, F-1 score, and mean average precision (mAP), were calculated, and loss values were analyzed to evaluate the model’s learning efficiency.
Results: The YOLOv8 model achieved a precision of 98.9%, recall of 98.9%, and accuracy of 99.5%. The mean average precision (mAP) reached 97.6%, indicating a high accuracy in detecting and localizing brain tumors. Loss value analysis demonstrated stable convergence during training, reflecting a robust model performance.
Conclusion: The YOLOv8-based approach provides a highly accurate and reliable method for detecting and localizing brain tumors in MRI. With exceptional precision, recall, and mAP, this model demonstrates significant potential for clinical applications, enabling faster and more precise neurooncological diagnosis and treatment planning.
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Personal views expressed by the contributors in their articles are not necessarily those of the Journal of Associated Medical Sciences, Faculty of Associated Medical Sciences, Chiang Mai University.
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