Classification of Body Constitution Based on TCM Philosophy and Deep Learning

Li, Yung‐Hui; Aslam, Muhammad Saqlain; Yang, Kai Lin; Kao, Chung An; Teng, Shin

doi:10.3390/sym12050803

Cited by 12 publications

(14 citation statements)

References 26 publications

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“…Yung-Hui Li defines 5 constitutions by the shapes of collarette. When collarette is positioned lesser than 1/3 diameter of iris the constitution is defined as Fire 20 . In this case study, the patient's collarette was shorter than 1/3 diameter of the iris, categorized as 'Fire.'.…”

Section: ⅲ Concl Usi On and Consi Derati Onmentioning

confidence: 99%

A Case Report of Hyperhidrosis with Sympathicotonia Detected by Iris Diagnosis

Wang¹,

Chae²,

Park³

et al. 2021

J Int Korean Med

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Objective: The aim of this case study was to describe a case of iris diagnosis of primary hyperhidrosis and the use of Korean medicine.Methods: A patient with symptoms of hyperhidrosis was diagnosed as having Taeeumin after assessment using the four basic Korean diagnostic methods. Iris diagnosis was used for further examination. The images obtained showed a remarkably defined collarette and increased nerve rings, which suggested an overactive sympathetic nerve system. Under the diagnosis of Taeeum, a Korean herbal medicine was prescribed with additional herbs to help alleviate the hyperactivity of the sympathetic nervous system.Results: The patient had been receiving treatment for hyperhidrosis for >30 years, with various medical attempts to relieve her symptoms, which were ineffective. She showed signs of improvement from day 4 into the treatment, and 80% of her symptoms were improved after completing a 6-week treatment course.Conclusion: The herbal medicine prescribed to the patient proved effective for reducing her chronic symptoms that had been unresponsive to previous medical treatments.

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Section: ⅲ Concl Usi On and Consi Derati Onmentioning

confidence: 99%

A Case Report of Hyperhidrosis with Sympathicotonia Detected by Iris Diagnosis

Wang¹,

Chae²,

Park³

et al. 2021

J Int Korean Med

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show abstract

“…An iris comprises a large amount of distinctive, constant, and forgery-proof features such as complex textures and explicit structural information for biometric identification [ 5 , 6 ]. In addition to this, iris traits are stable and remain unchanged throughout a person’s lifetime [ 7 ]. Hence, iris texture serves a key role in biometric practices.…”

Section: Introductionmentioning

confidence: 99%

“…A common iris recognition system often consists of the following procedures: Iris image acquisition, iris image pre-processing, iris segmentation, iris feature extraction, and feature matching for identification or authentication [ 1 , 3 , 4 , 6 , 10 ]. In the iris recognition system, iris segmentation is a critical and challenging task because of the unpredictable and irregular shape of the iris [ 4 , 7 ]. In general, iris textures are more visible near the area close to the pupil boundary.…”

Section: Introductionmentioning

confidence: 99%

Robust Iris Segmentation Algorithm in Non-Cooperative Environments Using Interleaved Residual U-Net

Putri

Aslam

et al. 2021

Sensors

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Iris segmentation plays an important and significant role in the iris recognition system. The prerequisite for accurate iris recognition is the correctness of iris segmentation. However, the efficiency and robustness of traditional iris segmentation methods are severely challenged in a non-cooperative environment because of unfavorable factors, for instance, occlusion, blur, low resolution, off-axis, motion, and specular reflections. All of the above factors seriously reduce the accuracy of iris segmentation. In this paper, we present a novel iris segmentation algorithm that localizes the outer and inner boundaries of the iris image. We propose a neural network model called “Interleaved Residual U-Net” (IRUNet) for semantic segmentation and iris mask synthesis. The K-means clustering is applied to select saliency points set in order to recover the outer boundary of the iris, whereas the inner border is recovered by selecting another set of saliency points on the inner side of the mask. Experimental results demonstrate that the proposed iris segmentation algorithm can achieve the mean IOU value of 98.9% and 97.7% for inner and outer boundary estimation, respectively, which outperforms the existing approaches on the challenging CASIA-Iris-Thousand database.

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“…All these domains are dealing with datasets and therefore demand high recognition accuracy with smaller computational complexity. It has been reported that deep learning models have more merits as compared to shallow neural designs [4][5][6][7][8][9]. Differences between DNN and SNN are described in Table 1.…”

Section: Introductionmentioning

confidence: 99%

An Effective and Improved CNN-ELM Classifier for Handwritten Digits Recognition and Classification

Ali

Pei

et al. 2020

Symmetry

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Optical character recognition is gaining immense importance in the domain of deep learning. With each passing day, handwritten digits (0–9) data are increasing rapidly, and plenty of research has been conducted thus far. However, there is still a need to develop a robust model that can fetch useful information and investigate self-build handwritten digit data efficiently and effectively. The convolutional neural network (CNN) models incorporating a sigmoid activation function with a large number of derivatives have low efficiency in terms of feature extraction. Here, we designed a novel CNN model integrated with the extreme learning machine (ELM) algorithm. In this model, the sigmoid activation function is upgraded as the rectified linear unit (ReLU) activation function, and the CNN unit along with the ReLU activation function are used as a feature extractor. The ELM unit works as the image classifier, which makes the perfect symmetry for handwritten digit recognition. A deeplearning4j (DL4J) framework-based CNN-ELM model was developed and trained using the Modified National Institute of Standards and Technology (MNIST) database. Validation of the model was performed through self-build handwritten digits and USPS test datasets. Furthermore, we observed the variation of accuracies by adding various hidden layers in the architecture. Results reveal that the CNN-ELM-DL4J approach outperforms the conventional CNN models in terms of accuracy and computational time.

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Classification of Body Constitution Based on TCM Philosophy and Deep Learning

Cited by 12 publications

References 26 publications

A Case Report of Hyperhidrosis with Sympathicotonia Detected by Iris Diagnosis

A Case Report of Hyperhidrosis with Sympathicotonia Detected by Iris Diagnosis

Robust Iris Segmentation Algorithm in Non-Cooperative Environments Using Interleaved Residual U-Net

An Effective and Improved CNN-ELM Classifier for Handwritten Digits Recognition and Classification

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