Using ghost convolution YOLO to detect brain tumor

Jiaxin Shi; Mingyue Xiang

doi:10.1117/12.3034790

11 July 2024 Using ghost convolution YOLO to detect brain tumor

Jiaxin Shi, Mingyue Xiang

Proceedings Volume 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024); 132102S (2024) https://doi.org/10.1117/12.3034790
Event: Third International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2024, Wuhan, China

Abstract

The prompt and accurate diagnosis of brain tumors is crucial for favorable patient prognoses. Advances in computer vision, particularly deep learning algorithms, have ushered in innovative methods for enhancing the diagnostic accuracy and automation of medical image analysis. You Only Look Once (YOLO) is a commonly employed technique in the field of image detection. And in this study, we used Ghost Convolution (GhostConv) enhances the YOLOv5 model by incorporating a new convolutional module to improve feature extraction, thereby enhancing model accuracy and detection speed. Through experiments, we have found that our model can achieve high accuracy and is suitable for practical medical detection.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jiaxin Shi and Mingyue Xiang "Using ghost convolution YOLO to detect brain tumor", Proc. SPIE 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024), 132102S (11 July 2024); https://doi.org/10.1117/12.3034790

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KEYWORDS

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Object detection

Brain

Cancer detection

Convolution

Education and training

Data modeling

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