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基于VGG-ST模型的奶牛粪便形态分类方法研究 被引量:1
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作者 纪宝锋 李斌 +2 位作者 卫勇 赵文文 周孟创 《农业机械学报》 EI CAS CSCD 北大核心 2023年第S01期245-251,共7页
快速准确识别奶牛粪便形态,对于奶牛肠胃健康监测与精细管理具有重要意义。针对目前奶牛粪便识别人工依赖强、识别难度大等问题,提出了一种基于VGG-ST(VGG-Swin Transformer)模型的奶牛稀便、软便、硬便及正常粪便图像识别与分类方法。... 快速准确识别奶牛粪便形态,对于奶牛肠胃健康监测与精细管理具有重要意义。针对目前奶牛粪便识别人工依赖强、识别难度大等问题,提出了一种基于VGG-ST(VGG-Swin Transformer)模型的奶牛稀便、软便、硬便及正常粪便图像识别与分类方法。首先,以泌乳期荷斯坦奶牛粪便为研究对象,采集上述4种不同形态的粪便图像共879幅,利用翻转、旋转等图像增强操作扩充至5580幅作为本研究数据集;然后,分别选取Swin Transformer、AlexNet、ResNet-34、ShuffleNet和MobileNet 5种典型深度学习图像分类模型进行奶牛粪便形态分类研究,通过对比分析,确定Swin Transformer为最优基础分类模型;最后,融合VGG模型与Swin Transformer模型,构建了VGG-ST模型,其中,VGG模型获取奶牛粪便局部特征,同时Swin Transformer模型提取全局自注意力特征,特征融合后实现奶牛粪便图像分类。实验结果表明,Swin Transformer模型在测试集中分类准确率达85.9%,与ShuffleNet、ResNet-34、MobileNet、AlexNet模型相比分别提高1.8、4.0、12.8、23.4个百分点;VGG-ST模型分类准确率达89.5%,与原Swin Transformer模型相比提高3.6个百分点。该研究可为奶牛粪便形态自动筛查机器人研发提供方法参考。 展开更多
关键词 奶牛 粪便分类 Swin Transformer 深度学习
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Similar fecal immunochemical test results in screening and referral colorectal cancer
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作者 Sietze T van Turenhout Leo GM van Rossum +10 位作者 Frank A Oort Robert JF Laheij Anne F van Rijn Jochim S Terhaar sive Droste Paul Fockens René WM van der Hulst Anneke A Bouman Jan BMJ Jansen Gerrit A Meijer Evelien Dekker Chris JJ Mulder 《World Journal of Gastroenterology》 SCIE CAS CSCD 2012年第38期5397-5403,共7页
AIM: To improve the interpretation of fecal immunochemical test (FIT) results in colorectal cancer (CRC) cases from screening and referral cohorts. METHODS: In this comparative observational study, two prospective coh... AIM: To improve the interpretation of fecal immunochemical test (FIT) results in colorectal cancer (CRC) cases from screening and referral cohorts. METHODS: In this comparative observational study, two prospective cohorts of CRC cases were compared. The first cohort was obtained from 10 322 average risk subjects invited for CRC screening with FIT, of which, only subjects with a positive FIT were referred for colonoscopy. The second cohort was obtained from 3637 subjects scheduled for elective colonoscopy with a positive FIT result. The same FIT and positivity threshold (OC sensor; ≥ 50 ng/mL) was used in both cohorts. Colonoscopy was performed in all referral subjects and in FIT positive screening subjects. All CRC cases were selected from both cohorts. Outcome measurements were mean FIT results and FIT scores per tissue tumor stage (T stage). RESULTS: One hundred and eighteen patients with CRC were included in the present study: 28 cases obtained from the screening cohort (64% male; mean age 65 years, SD 6.5) and 90 cases obtained from the referral cohort (58% male; mean age 69 years, SD 9.8). The mean FIT results found were higher in the referral cohort (829 ± 302 ng/mLvs 613 ± 368 ng/mL,P = 0.02). Tissue tumor stage (T stage) distribution was dif-ferent between both populations [screening population: 13 (46%) T1, eight (29%) T2, six (21%) T3, one (4%) T4 carcinoma; referral population: 12 (13%) T1, 22 (24%) T2, 52 (58%) T3, four (4%) T4 carcinoma], and higher T stage was significantly associated with higher FIT results (P < 0.001). Per tumor stage, no significant difference in mean FIT results was observed (screening vs referral: T1 498 ± 382 ng/mL vs 725 ± 374 ng/mL, P = 0.22; T2 787 ± 303 ng/mL vs 794 ± 341 ng/mL, P = 0.79; T3 563 ± 368 ng/mLvs 870 ± 258 ng/mL,P = 0.13; T4 not available). After correction for T stage in logistic regression analysis, no significant differences in mean FIT results were observed between both types of cohorts (P = 0.10). CONCLUSION: Differences in T stage distribution largely explain differences in FIT results between screening and referral cohorts. Therefore, FIT results should be reported according to T stage. 展开更多
关键词 Screening population Referral cohort Fecal immunochemical test Tumor stage distribution Colorectal cancer
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