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Proceedings of the 2024 Ninth International Conference on Research in Intelligent Computing in Engineering

Annals of Computer Science and Information Systems, Volume 42

Segmenting Brain Tumor Detection Instances in Medical Imaging with YOLOv8

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DOI: http://dx.doi.org/10.15439/2024R89

Citation: Proceedings of the 2024 Ninth International Conference on Research in Intelligent Computing in Engineering, Vijender Kumar Solanki, Tran Duc Tan, Pradeep Kumar, Manuel Cardona (eds). ACSIS, Vol. 42, pages 35–38 ()

Full text

Abstract. Because of their complexity and the urgent requirement for accurate diagnosis, brain tumors pose a serious challenge in medical diagnostics. This study presents a novel method for detecting brain tumors in medical imaging by employing instance segmentation with the sophisticated Yolov8 model. We start by outlining how inaccurately current imaging methods can detect brain cancers. Following the detailed explanation of the YOLOv8 architecture specialized for this study, we delve into explaining our method entailing a thorough data preparation strategy designed for medical imaging. We go into great detail with our training and validation procedure and emphasize what needed to be changed in order to handle medical datasets. The results section shows the effectiveness of the model using various metrics such as accuracy, precision, recall, and F1-score, all indicating notable gains compared to current techniques. The conclusion of the paper reflects on the potential significance of using YOLOv8 in medical imaging for the detection of brain tumors and suggests a quantum leap in oncological diagnostics and the care of patients.

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