• Title/Summary/Keyword: dense

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A computer vision-based approach for behavior recognition of gestating sows fed different fiber levels during high ambient temperature

  • Kasani, Payam Hosseinzadeh;Oh, Seung Min;Choi, Yo Han;Ha, Sang Hun;Jun, Hyungmin;Park, Kyu hyun;Ko, Han Seo;Kim, Jo Eun;Choi, Jung Woo;Cho, Eun Seok;Kim, Jin Soo
    • Journal of Animal Science and Technology
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    • v.63 no.2
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    • pp.367-379
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    • 2021

  • The objectives of this study were to evaluate convolutional neural network models and computer vision techniques for the classification of swine posture with high accuracy and to use the derived result in the investigation of the effect of dietary fiber level on the behavioral characteristics of the pregnant sow under low and high ambient temperatures during the last stage of gestation. A total of 27 crossbred sows (Yorkshire × Landrace; average body weight, 192.2 ± 4.8 kg) were assigned to three treatments in a randomized complete block design during the last stage of gestation (days 90 to 114). The sows in group 1 were fed a 3% fiber diet under neutral ambient temperature; the sows in group 2 were fed a diet with 3% fiber under high ambient temperature (HT); the sows in group 3 were fed a 6% fiber diet under HT. Eight popular deep learning-based feature extraction frameworks (DenseNet121, DenseNet201, InceptionResNetV2, InceptionV3, MobileNet, VGG16, VGG19, and Xception) used for automatic swine posture classification were selected and compared using the swine posture image dataset that was constructed under real swine farm conditions. The neural network models showed excellent performance on previously unseen data (ability to generalize). The DenseNet121 feature extractor achieved the best performance with 99.83% accuracy, and both DenseNet201 and MobileNet showed an accuracy of 99.77% for the classification of the image dataset. The behavior of sows classified by the DenseNet121 feature extractor showed that the HT in our study reduced (p < 0.05) the standing behavior of sows and also has a tendency to increase (p = 0.082) lying behavior. High dietary fiber treatment tended to increase (p = 0.064) lying and decrease (p < 0.05) the standing behavior of sows, but there was no change in sitting under HT conditions.

  • https://doi.org/10.5187/jast.2021.e35 인용 PDF KSCI HTML

Detection of Plastic Greenhouses by Using Deep Learning Model for Aerial Orthoimages (딥러닝 모델을 이용한 항공정사영상의 비닐하우스 탐지)

  • Byunghyun Yoon;Seonkyeong Seong;Jaewan Choi
    • Korean Journal of Remote Sensing
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    • v.39 no.2
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    • pp.183-192
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    • 2023

  • The remotely sensed data, such as satellite imagery and aerial photos, can be used to extract and detect some objects in the image through image interpretation and processing techniques. Significantly, the possibility for utilizing digital map updating and land monitoring has been increased through automatic object detection since spatial resolution of remotely sensed data has improved and technologies about deep learning have been developed. In this paper, we tried to extract plastic greenhouses into aerial orthophotos by using fully convolutional densely connected convolutional network (FC-DenseNet), one of the representative deep learning models for semantic segmentation. Then, a quantitative analysis of extraction results had performed. Using the farm map of the Ministry of Agriculture, Food and Rural Affairsin Korea, training data was generated by labeling plastic greenhouses into Damyang and Miryang areas. And then, FC-DenseNet was trained through a training dataset. To apply the deep learning model in the remotely sensed imagery, instance norm, which can maintain the spectral characteristics of bands, was used as normalization. In addition, optimal weights for each band were determined by adding attention modules in the deep learning model. In the experiments, it was found that a deep learning model can extract plastic greenhouses. These results can be applied to digital map updating of Farm-map and landcover maps.

  • https://doi.org/10.7780/kjrs.2023.39.2.5 인용 PDF HTML

Study on the Usefulness about Molecular Breast Imaging In Dense Breast (치밀형 유방에서 Molecular Breast Imaging 검사의 유용성에 관한 고찰)

  • Baek, Song Ee;Kang, Chun Goo;Lee, Han Wool;Park, Min Soo;Choi, Young Sook;Kim, Jae Sam
    • The Korean Journal of Nuclear Medicine Technology
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    • v.20 no.1
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    • pp.42-46
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    • 2016

  • Purpose Mammography is the most widely used scan for the early diagnosis since it is possible to observe the anatomy of the breast. however, The sensitivity is markedly reduced in high-risk patients with dense breast. Molecular Breast Imaging (MBI) sacn is possible to get the high resolution functional imaging, and This new neclear medicine technique get the more improved diagnostic information through It is useful for confirmation of tumor's location in dense breast. The purpose of this study is to evaluate the usefulness of MBI for tumor diagnosis in patients with dense breast. Materials and Methods We investigated 10 patients female breast cancer with dense breast type who had visited the hospital from September 1st to Octorber 10th, 2015. The patients underwent both MBI and Mammography. MBI (Discovery 750B; General Electric Healthcare, USA) scan was 99mTc-MIBI injected with 20 mCi on the opposite side of the arm with the lesions, after 20 minutes, gained bilateral breast CC (CranioCaudal), MLO (Medio Lateral Oblique) View. Mammography was also conducted in the same posture. MBI and Mammography images were compared to evaluate the sensitivity and specificity of each case utilizing both image and two images in blind tests. Results The results of the blind test for breast cancer showed that the sensitivity of Mammography, MBI scan was 63%, 89%, respectively, and that their specificity was 38%, 87%, respectively. Using both the Mammography and MBI scan was Sensitivity 92%, specificity 90%. Conclusion This research has found that, The tumor of dense tissue that can not easily distinguishable in Mammography is possible to more accurate diagnosis since It is easy to visually evaluation. But MBI sacn has difficulty imaging microcalcificatons, If used in conjunction with mammography it is thought to give provide more diagnostic information.

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