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

Li, Jinxing; Zhang, David; Li, Yongcheng; Wu, Jian; Zhang, Bob

doi:10.1016/j.ins.2016.09.031

Cited by 46 publications

(22 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using e-nose is a less expensive and more portable way for breath analysis. Recently, e-nose has gradually been used in medicine for the diagnosis of renal disease [29], diabetes [30], lung cancer [31], and asthma [32]. Though all of these methods work satisfactorily in breath analysis, their results could possibly be improved.…”

Section: Breath Biomarker and Diseasesmentioning

confidence: 99%

Breath Analysis for Detecting Diseases on Respiratory, Metabolic and Digestive System

Kou¹,

Zhang²,

You³

et al. 2019

JBiSE

Self Cite

View full text Add to dashboard Cite

Recently, biological technology and computer science are of great importance in medical applications. Since one's breath biomarkers have been proved to be related with diseases, it is possible to detect diseases by analysis of breath samples captured by e-noses. In this paper, a novel medical e-nose system specific to disease diagnosis was used to collect a large-scale breath dataset. Methods for signal processing, feature extracting as well as feature & sensor selection were discussed for detecting diseases on respiratory, metabolic and digestive system. Sequential forward selection is used to select the best combination of sensors and features. The experimental results showed that the proposed system was able to well distinguish healthy samples and samples with different diseases. The results also showed the most significant sensors and features for different tasks, which meets the relationship between diseases and breath biomarkers. By selecting best combination of different sensors and features for different tasks, the e-nose system is shown to be helpful and effective for diseases diagnosis on respiratory, metabolic and digestive system. Open Access https://doi.org/10.4236/jbise.2019.121004 40 J. Biomedical Science and Engineering Breath Analysis with E-NoseCurrently, the measurement of exhaled breath is usually performed by two common gas analysis apparatuses, gas chromatography (GC) [26] or electronic nose (e-nose) [27]. GC can separate and identify molecules that are responsible for typical odors occurring in specific diseases, which is very accurate for

show abstract

Section: Breath Biomarker and Diseasesmentioning

confidence: 99%

Breath Analysis for Detecting Diseases on Respiratory, Metabolic and Digestive System

Kou¹,

Zhang²,

You³

et al. 2019

JBiSE

Self Cite

View full text Add to dashboard Cite

show abstract

“…A relaxed collaborative representation (RCR) [38] was proposed through minimizing the sum of the total distance between each view coefficient and its average value. Li et al [39] proposed a joint similar and specific learning (JSSL) algorithm via separating the sparse representation coefficients into two components: the similar and specific ones. A joint discriminant collaborative representation (JDCR) was introduced in [40] through obtaining the multi-view representation coefficients discriminatively.…”

Section: A Multi-view Learningmentioning

confidence: 99%

Multi-View Classification via a Fast and Effective Multi-View Nearest-Subspace Classifier

Shu¹,

Zhang²,

Tang³

2019

IEEE Access

Self Cite

View full text Add to dashboard Cite

Multi-view data represented in multiple views contains more complementary information than a single view, whereby multi-view learning explores and utilizes the multi-view data. In general, most existing multi-view learning methods consider the correlation between multiple views. However, the relationship between classes and views which is also important in multi-view learning has never been involved in the existing works. In this paper, we propose a fast and effective multi-view nearest-subspace classifier (MV-NSC) by taking advantage of both the two relationships simultaneously. MV-NSC consists of four main parts: 1) projection residual, 2) view-dependent class separability, 3) view similarity, and 4) final decision. The last part combines the first three parts in one final decision matrix, while the first three parts utilize the information of the multi-view data in various aspects. Our proposed method is evaluated on four benchmark datasets and compared with seven other classifiers including both multi-and single-view algorithms. According to the experimental results, it shows that our proposed method is effective, efficient, and robust in multi-view classification.INDEX TERMS Adaption model, classification algorithm, multi-view data, nearest-subspace classifier, view-dependent class separability, view similarity.

show abstract

“…Tongue imaging has caused increasing research interests in the last two decades [1]- [14]. Compared with mainstream medical imaging approaches, tongue image analysis has the advantages of cheap and convenient imaging yet accurate diagnosis [3]- [5], [10].…”

Section: Introductionmentioning

confidence: 99%

Tongue Image Alignment via Conformal Mapping for Disease Detection

Zhang²,

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

Tongue image analysis has been an active study in medical imaging. Existing tongue image processing approaches deal with the issue of image alignment in oversimplified ways. These approaches mainly extract patches or simple regions on pre-defined positions, which are severely sensitive to tongue deformations. In this paper, we present a conformal mapping method for tongue image alignment, the principle of which is to determine the interior mapping based on the boundary mapping so that it is robust to the deformations. The conformal alignment consists of two stages: the mapping on the boundary is firstly established via the Fourier descriptor before the mapping is extended onto the interior region via Cauchy's integral and finite-difference method. Average tongues and eigen-tongues are constructed based on the conformal alignment for feature extraction. Experiments show that the proposed alignment is robust against tongue deformations and can be employed to correct existing rigid partition methods. Numerical evaluations on time efficiency and accuracy also show that our method is considerably fast and very accurate, compared with several baseline methods in this field. For the task of disease detection, the features based on the aligned images outperform some state-of-the-art features. The results reveal that the proposed method provides an efficient and accurate tool for deformable medical image alignment and disease diagnosis. A MatLab script of the proposed algorithm is available on https://codeocean.com/capsule/4382908/tree/v1. INDEX TERMS Image alignment, conformal mapping, disease detection.

show abstract

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

Cited by 46 publications

References 28 publications

Breath Analysis for Detecting Diseases on Respiratory, Metabolic and Digestive System

Breath Analysis for Detecting Diseases on Respiratory, Metabolic and Digestive System

Multi-View Classification via a Fast and Effective Multi-View Nearest-Subspace Classifier

Tongue Image Alignment via Conformal Mapping for Disease Detection

Contact Info

Product

Resources

About