A Proposal of Heart Diseases Diagnosis Method Using Analysis of Face Color

Kim, Bong-hyun; Lee, Se-Hwan; Cho, Dong-Uk; Oh, Sang-Young

doi:10.1109/alpit.2008.27

Cited by 14 publications

(12 citation statements)

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“…Moreover, extracted color, texture and geometry features of tongue feature were exploited to detect DM and arXiv:1604.03443v1 [cs.CV] 12 Apr 2016 nonproliferative diabetic retinopathy (DR) [5]. A heart disease diagnostic system based on facial color [18] was proposed. Five facial blocks were extracted from the facial image to detect hepatitis in [19], and the average accuracy achieved 73.6% using the average RGB pixel intensities features.…”

Section: Introductionmentioning

confidence: 99%

Joint similar and specific learning for diabetes mellitus and impaired glucose regulation detection

Zhang

et al. 2017

Information Sciences

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Section: Introductionmentioning

confidence: 99%

Joint similar and specific learning for diabetes mellitus and impaired glucose regulation detection

Zhang

et al. 2017

Information Sciences

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“…In [13], they extracted the center forehead and lips of a person and analyzed the red color value distribution of the center forehead and lips. The authors wanted to survey real clinical data of HD patients and group them into different cases based on the analysis that facial color can help doctors diagnose HD.…”

Section: Introductionmentioning

confidence: 99%

Effective Heart Disease Detection Based on Quantitative Computerized Traditional Chinese Medicine Using Representation Based Classifiers

Shu

Zhang

Tang

2017

Evidence-Based Complementary and Alternative Medicine

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At present, heart disease is the number one cause of death worldwide. Traditionally, heart disease is commonly detected using blood tests, electrocardiogram, cardiac computerized tomography scan, cardiac magnetic resonance imaging, and so on. However, these traditional diagnostic methods are time consuming and/or invasive. In this paper, we propose an effective noninvasive computerized method based on facial images to quantitatively detect heart disease. Specifically, facial key block color features are extracted from facial images and analyzed using the Probabilistic Collaborative Representation Based Classifier. The idea of facial key block color analysis is founded in Traditional Chinese Medicine. A new dataset consisting of 581 heart disease and 581 healthy samples was experimented by the proposed method. In order to optimize the Probabilistic Collaborative Representation Based Classifier, an analysis of its parameters was performed. According to the experimental results, the proposed method obtains the highest accuracy compared with other classifiers and is proven to be effective at heart disease detection.

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“…The abnormal supply of the blood and oxygen to the brain can result in the patient having different facial color and texture complexion than someone that is healthy. Therefore, some researchers [ 11 , 12 , 13 , 14 , 15 , 16 ] have carried out many related works to diagnose disease based on facial color and texture features.…”

Section: Introductionmentioning

confidence: 99%

Novel Noninvasive Brain Disease Detection System Using a Facial Image Sensor

Shu

Zhang

Tang

2017

Sensors

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Brain disease including any conditions or disabilities that affect the brain is fast becoming a leading cause of death. The traditional diagnostic methods of brain disease are time-consuming, inconvenient and non-patient friendly. As more and more individuals undergo examinations to determine if they suffer from any form of brain disease, developing noninvasive, efficient, and patient friendly detection systems will be beneficial. Therefore, in this paper, we propose a novel noninvasive brain disease detection system based on the analysis of facial colors. The system consists of four components. A facial image is first captured through a specialized sensor, where four facial key blocks are next located automatically from the various facial regions. Color features are extracted from each block to form a feature vector for classification via the Probabilistic Collaborative based Classifier. To thoroughly test the system and its performance, seven facial key block combinations were experimented. The best result was achieved using the second facial key block, where it showed that the Probabilistic Collaborative based Classifier is the most suitable. The overall performance of the proposed system achieves an accuracy −95%, a sensitivity −94.33%, a specificity −95.67%, and an average processing time (for one sample) of <1 min at brain disease detection.

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A Proposal of Heart Diseases Diagnosis Method Using Analysis of Face Color

Cited by 14 publications

References 0 publications

Joint similar and specific learning for diabetes mellitus and impaired glucose regulation detection

Joint similar and specific learning for diabetes mellitus and impaired glucose regulation detection

Effective Heart Disease Detection Based on Quantitative Computerized Traditional Chinese Medicine Using Representation Based Classifiers

Novel Noninvasive Brain Disease Detection System Using a Facial Image Sensor

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