StoolNet for Color Classification of Stool Medical Images

Yang, Ziyuan; Leng, Lu; Kim, Byung-Gyu

doi:10.3390/electronics8121464

Cited by 33 publications

(35 citation statements)

References 28 publications

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“…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. In [ 44 ], the method is designed for fecal color recognition, but the main objective of this paper was to design a quick, automatic, accurate, and robust method to classify the traits of fecal images.…”

Section: Experiments and Discussionmentioning

confidence: 99%

“…Furthermore, there are several substantial differences between this paper and [ 44 ], which are summarized as follows: The recognition tasks are different. In [ 44 ], the method is designed for fecal color recognition, but the main objective of this paper was to design a quick, automatic, accurate, and robust method to classify the traits of fecal images. Color recognition and trait recognition are both important for macroscopic examinations, but typically trait recognition is more difficult than color recognition; The method described in [ 44 ] cannot maintain its level of performance in the task presented in this paper, which is demonstrated by the experimental results.…”

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.…”

Section: Experiments and Discussionmentioning

confidence: 99%

“…Nkamgang et al [ 43 ] extracted histogram orientation gradient (HOG) features from the microscopic images and applied a neuro-fuzzy classifier for the diagnosis of intestinal parasite diseases. Yang et al [ 44 ] proposed a shallow CNN dubbed StoolNet to classify the colors of fecal images.…”

Section: Related Workmentioning

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%

See 2 more Smart Citations

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

Leng

Yang

Kim

et al. 2020

Sensors

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“…The employment of computer-aided diagnosis systems optimized the performance of the breast cancer diagnosis [9]. Recently, Deep Learning (DL) has played the main role in several medical tasks [10][11][12][13], and the classification and detection of breast cancer [14,15]. The breast cancer classification task is challenging due the complexity of the breast cancer images.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Performance of Breast Cancer Classification by Employing the Same Domain Transfer Learning from Hybrid Deep Convolutional Neural Network Model

et al. 2020

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Breast cancer is a significant factor in female mortality. An early cancer diagnosis leads to a reduction in the breast cancer death rate. With the help of a computer-aided diagnosis system, the efficiency increased, and the cost was reduced for the cancer diagnosis. Traditional breast cancer classification techniques are based on handcrafted features techniques, and their performance relies upon the chosen features. They also are very sensitive to different sizes and complex shapes. However, histopathological breast cancer images are very complex in shape. Currently, deep learning models have become an alternative solution for diagnosis, and have overcome the drawbacks of classical classification techniques. Although deep learning has performed well in various tasks of computer vision and pattern recognition, it still has some challenges. One of the main challenges is the lack of training data. To address this challenge and optimize the performance, we have utilized a transfer learning technique which is where the deep learning models train on a task, and then fine-tune the models for another task. We have employed transfer learning in two ways: Training our proposed model first on the same domain dataset, then on the target dataset, and training our model on a different domain dataset, then on the target dataset. We have empirically proven that the same domain transfer learning optimized the performance. Our hybrid model of parallel convolutional layers and residual links is utilized to classify hematoxylin–eosin-stained breast biopsy images into four classes: invasive carcinoma, in-situ carcinoma, benign tumor and normal tissue. To reduce the effect of overfitting, we have augmented the images with different image processing techniques. The proposed model achieved state-of-the-art performance, and it outperformed the latest methods by achieving a patch-wise classification accuracy of 90.5%, and an image-wise classification accuracy of 97.4% on the validation set. Moreover, we have achieved an image-wise classification accuracy of 96.1% on the test set of the microscopy ICIAR-2018 dataset.

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