Detection and Classification of Human Stool Using Deep Convolutional Neural Networks

Choy, Yin Pui; Hu, Guoqing; Chen, Jia

doi:10.1109/access.2021.3131982

Cited by 5 publications

(4 citation statements)

References 48 publications

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“…Other methods have also been proposed for object size detection, such as wavelet-based methods (Peng et al, 2019) [8] , shape context-based methods (Luo et al, 2018) [9] , and adaptive threshold-based methods (Chen et al, 2019) [10] .…”

Section: Related Workmentioning

confidence: 99%

Object size detection based on image processing

Zhao,

Shi

2023

Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023)

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This paper proposes a novel method for detecting object sizes in images. Traditional methods have limitations due to image quality, which affects detection accuracy. To address this problem, a new approach is proposed that combines Zernike moments and deep learning models. This approach combines the strengths of both methods and overcomes the limitations of traditional approaches. The proposed approach improves the accuracy of object size detection and is robust to image noise and distortion. Experimental results demonstrate the effectiveness of this approach, which outperforms existing methods in terms of accuracy and robustness.

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Section: Related Workmentioning

confidence: 99%

Object size detection based on image processing

Zhao,

Shi

2023

Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023)

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“…Yang et al [14] proposed StoolNet to analyze colors in macroscopic feces images in order to judge patients' intestinal health. Choy et al [15] were able to improve the accuracy of StoolNet for fecal traits. Inspired by StoolNet, Leng et al [16] developed a lightweight classification network to identify feces traits, achieving satisfactory performance.…”

Section: Medical Image Diagnosismentioning

confidence: 99%

E-Health Self-Help Diagnosis from Feces Images in Real Scenes

Wan

Leng

et al. 2023

Electronics

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Deep learning models and computer vision are commonly integrated for e-health self-help diagnosis. The abnormal colors and traits of feces can reveal the risks of cancer and digestive diseases. As such, this paper develops a self-help diagnostic system to conveniently analyze users’ health conditions from feces images at home, which can reduce dependence on professional skills and examinations equipment. Unfortunately, real scenes at home suffer from several severe challenges, including the lack of labeled data, complex backgrounds, varying illumination, etc. A semi-supervised learning strategy is employed to solve the scarcity of labeled data and reduce the burden of manual labeling. The unlabeled data are classified by an initial model that is pretrained on a small number of training data. Then, the labels with high confidence are allocated to the unlabeled samples in order to extend the training data accordingly. With regard to the small feces areas in certain samples, an adaptive upsampling method is proposed to enlarge the suitable local area according to the proportion of the foreground. Synthesized feces images in real scenes are tested to confirm the effectiveness and efficiency of the proposed method. In terms of accuracy, our proposed model can achieve 100% and 99.2% on color and trait recognition in medical scenes, respectively, and 99.1% and 100% on color and trait recognition in real scenes, respectively. The related datasets and codes will be released on Github.

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“…Leng et al [57] achieved a classification accuracy of 98.4% on a self-built dataset of five stool classifications through a lightweight shallow CNN. Choy et al [58] used ResNext-50 to classify human feces for monitoring and diagnosis of human diseases, achieving an accuracy of 94.35%.…”

Section: Related Workmentioning

confidence: 99%

A Fine-Grained Image Classification Approach for Dog Feces Using MC-SCMNet under Complex Backgrounds

Liang

Cai

et al. 2023

Animals

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In a natural environment, factors such as weathering and sun exposure will degrade the characteristics of dog feces; disturbances such as decaying wood and dirt are likely to make false detections; the recognition distinctions between different kinds of feces are slight. To address these issues, this paper proposes a fine-grained image classification approach for dog feces using MC-SCMNet under complex backgrounds. First, a multi-scale attention down-sampling module (MADM) is proposed. It carefully retrieves tiny feces feature information. Second, a coordinate location attention mechanism (CLAM) is proposed. It inhibits the entry of disturbance information into the network’s feature layer. Then, an SCM-Block containing MADM and CLAM is proposed. We utilized the block to construct a new backbone network to increase the efficiency of fecal feature fusion in dogs. Throughout the network, we decrease the number of parameters using depthwise separable convolution (DSC). In conclusion, MC-SCMNet outperforms all other models in terms of accuracy. On our self-built DFML dataset, it achieves an average identification accuracy of 88.27% and an F1 value of 88.91%. The results of the experiments demonstrate that it is more appropriate for dog fecal identification and maintains stable results even in complex backgrounds, which may be applied to dog gastrointestinal health checks.

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Detection and Classification of Human Stool Using Deep Convolutional Neural Networks

Cited by 5 publications

References 48 publications

Object size detection based on image processing

Object size detection based on image processing

E-Health Self-Help Diagnosis from Feces Images in Real Scenes

A Fine-Grained Image Classification Approach for Dog Feces Using MC-SCMNet under Complex Backgrounds

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