Three-category colorectal lesion image automatic detection based on G-YOLOv8

Junru Liang; Junlong Li

doi:10.1117/12.3013254

29 November 2023 Three-category colorectal lesion image automatic detection based on G-YOLOv8

Junru Liang, Junlong Li

Proceedings Volume 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023); 129370S (2023) https://doi.org/10.1117/12.3013254
Event: International Conference on Internet of Things and Machine Learning (IoTML 2023), 2023, Singapore, Singapore

Abstract

With the increasing number of colorectal cancer patients worldwide, colorectal cancer has become one of the leading causes of cancer death. Therefore, it is particularly important to improve the detection rate of colorectal lesions. Aiming at the misdetection and missed detection of assistant physicians in lesion detection, this paper proposes a colorectal lesion detection task based on improved YOLOv8. The sample data set is constructed through the private data set (SSPH data set) and the public data set (CVC-Polyp), and the improved YOLOv8 model is trained by selecting the SGD optimizer and transfer learning method. The introduction of the GAM attention mechanism on the basis of the YOLOv8 detection model can better focus on inter-dimensional information interaction than other attention mechanisms, and improve the model's attention to lesions. The experimental results show that the method proposed in this paper has an precision of 94.9%, a recall rate of 88%, and a Map@0.5 value of 95.8%. Compared with the original YOLOv8 model, it has been effectively improved.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Junru Liang and Junlong Li "Three-category colorectal lesion image automatic detection based on G-YOLOv8", Proc. SPIE 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023), 129370S (29 November 2023); https://doi.org/10.1117/12.3013254

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