Image detection method for multi-category lesions in wireless capsule endoscopy based on deep learning models

doi:10.3748/wjg.v30.i48.5111

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Dec 28, 2024 (publication date) through Dec 2, 2024

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World Journal of Gastroenterology

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1007-9327

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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World J Gastroenterol. Dec 28, 2024; 30(48): 5111-5129
Published online Dec 28, 2024. doi: 10.3748/wjg.v30.i48.5111

Table 6 Experimental results of different attention modules

Method	CBAM	SE	VIT	Swin Transformer	mAP50	GFLOPS	FPS
Method 1	Y	N	N	N	88.9	133.6	163.96
Method 2	N	Y	N	N	87.2	121.5	173.73
Method 3	N	N	Y	N	89.2	219.9	103.76
Method 4	N	N	N	Y	90.7	159.7	149.58

Y: Means that the module is designed on the original YOLOv8 algorithm for the experiment; N: Means that the module was not used in the model design.

Citation: Xiao ZG, Chen XQ, Zhang D, Li XY, Dai WX, Liang WH. Image detection method for multi-category lesions in wireless capsule endoscopy based on deep learning models. World J Gastroenterol 2024; 30(48): 5111-5129
URL: https://www.wjgnet.com/1007-9327/full/v30/i48/5111.htm
DOI: https://dx.doi.org/10.3748/wjg.v30.i48.5111