Mo2049 – Augmenting Gastrointestinal Health: A Deep Learning Approach to Human Stool Recognition and Characterization in Macroscopic Images

Hachuel, David; Jha, Akshay; Estrin, Deborah; Martinez, Alfonso; Staller, Kyle; Vélez, Christopher

doi:10.1016/s0016-5085(19)39304-7

Cited by 12 publications

(21 citation statements)

References 11 publications

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Section: Experiments and Discussionmentioning

confidence: 99%

“…There are very few fecal trait recognition methods, so we compared our method with the method in [ 2 ] and the related state of the art [ 44 ]. In [ 2 ], the method was not designed for a real hospital environment. Furthermore, there are several substantial differences between this paper and [ 44 ], which are summarized as follows: The recognition tasks are different.…”

Section: Experiments and Discussionmentioning

confidence: 99%

“…Besides, when the training set was composed of the images under single illumination, our method yields at least a 13.96% higher accuracy than the other methods. The method described in [ 2 ] was designed for uncontrollable environments, which means that the structure is too bloated to converge in controllable environments. In addition, the number of classes in our dataset is more than in their dataset, which implies that the classification difficulty was higher for our dataset in the task.…”

Section: Experiments and Discussionmentioning

confidence: 99%

“…Few researchers have investigated computer-aided fecal examinations because of the lack of datasets. Hachuel et al [ 2 ] applied ResNet [ 42 ] to classify feces macroscopic images into three classes, namely, constipation, normal, and loose. The images were collected in uncontrollable environments.…”

Section: Related Workmentioning

confidence: 99%

“…Because of the scarcity of fecal trait datasets labeled by professional doctors, few related automatic diagnosis systems have been designed and this research field is developing slowly. To our best knowledge, the only report on fecal trait recognition is found in [ 2 ]. However, this method cannot maintain its recognition performance in real hospital environments because the dataset was collected in uncontrollable environments.…”

Section: Introductionmentioning

confidence: 99%

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A Light-Weight Practical Framework for Feces Detection and Trait Recognition

Leng

Yang

Kim

et al. 2020

Sensors

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Fecal trait examinations are critical in the clinical diagnosis of digestive diseases, and they can effectively reveal various aspects regarding the health of the digestive system. An automatic feces detection and trait recognition system based on a visual sensor could greatly alleviate the burden on medical inspectors and overcome many sanitation problems, such as infections. Unfortunately, the lack of digital medical images acquired with camera sensors due to patient privacy has obstructed the development of fecal examinations. In general, the computing power of an automatic fecal diagnosis machine or a mobile computer-aided diagnosis device is not always enough to run a deep network. Thus, a light-weight practical framework is proposed, which consists of three stages: illumination normalization, feces detection, and trait recognition. Illumination normalization effectively suppresses the illumination variances that degrade the recognition accuracy. Neither the shape nor the location is fixed, so shape-based and location-based object detection methods do not work well in this task. Meanwhile, this leads to a difficulty in labeling the images for training convolutional neural networks (CNN) in detection. Our segmentation scheme is free from training and labeling. The feces object is accurately detected with a well-designed threshold-based segmentation scheme on the selected color component to reduce the background disturbance. Finally, the preprocessed images are categorized into five classes with a light-weight shallow CNN, which is suitable for feces trait examinations in real hospital environments. The experiment results from our collected dataset demonstrate that our framework yields a satisfactory accuracy of 98.4%, while requiring low computational complexity and storage.

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Section: Experiments and Discussionmentioning

confidence: 99%

Section: Experiments and Discussionmentioning

confidence: 99%

Section: Experiments and Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Light-Weight Practical Framework for Feces Detection and Trait Recognition

Leng

Yang

Kim

et al. 2020

Sensors

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A computer-aid multi-task light-weight network for macroscopic feces diagnosis

Yang

Leng

et al. 2022

Multimed Tools Appl

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The abnormal traits and colors of feces typically indicate that the patients are probably suffering from tumor or digestive-system diseases. Thus a fast, accurate and automatic health diagnosis system based on feces is urgently necessary for improving the examination speed and reducing the infection risk. The rarity of the pathological images would deteriorate the accuracy performance of the trained models. In order to alleviate this problem, we employ augmentation and over-sampling to expand the samples of the classes that have few samples in the training batch. In order to achieve an impressive recognition performance and leverage the latent correlation between the traits and colors of feces pathological samples, a multi-task network is developed to recognize colors and traits of the macroscopic feces images. The parameter number of a single multi-task network is generally much smaller than the total parameter number of multiple single-task networks, so the storage cost is reduced. The loss function of the multi-task network is the weighted sum of the losses of the two tasks. In this paper, the weights of the tasks are determined according to their difficulty levels that are measured by the fitted linear functions. The sufficient experiments confirm that the proposed method can yield higher accuracies, and the efficiency is also improved.

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Generative adversarial networks for histopathology staining

Ganesh

George

2021

Generative Adversarial Networks for Image-to-Image Translation

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Mo2049 – Augmenting Gastrointestinal Health: A Deep Learning Approach to Human Stool Recognition and Characterization in Macroscopic Images

Cited by 12 publications

References 11 publications

A Light-Weight Practical Framework for Feces Detection and Trait Recognition

A Light-Weight Practical Framework for Feces Detection and Trait Recognition

A computer-aid multi-task light-weight network for macroscopic feces diagnosis

Generative adversarial networks for histopathology staining

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