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DsNet: Dual stack network for detecting diabetes mellitus and chronic kidney disease
Zhang,Qi1; Zhou,Jianhang1; Zhang,Bob1; Wu,Enhua2
2021-02-08
Source PublicationInformation Sciences
ISSN0020-0255
Volume547Pages:945-962
Abstract

Diabetes mellitus and chronic kidney disease are two severe chronic diseases in the world, affecting the quality of a patient's life. However, detecting these two diseases often applies professional medical techniques such as a Fasting Plasma Glucose test and estimating the glomerular filtration rate (eGFR) measurement, which usually requires a blood test. Given the various inconveniences and risks in existing conventional diagnostic approaches, noninvasive healthcare systems based on intelligent electronic detection/prevention are preferred. To achieve this goal, we propose a progressively trainable network, i.e., dual stack network (DsNet), to distinguish patients with chronic kidney disease, diabetes mellitus from healthy people simultaneously through analyzing the facial images of candidates. The first stack subnetwork extracts high-level representative features from the facial images effectively. While the second stack subnetwork can further analyze the extracted high-level features from the first stack subnetwork, before classifying the two diseases from healthy individuals simultaneously. Extensive experiments on a dataset with 229 healthy samples, 236 diabetes, and 200 chronic kidney disease patients show that our proposed method generated the F1-score of 95.33%, 98.17%, and 94.67% for detecting chronic kidney disease, diabetes, and healthy samples respectively. Our proposed DsNet achieves significant improvements compared with other traditional noninvasive detection approaches.

KeywordChronic Kidney Disease Diabetes Mellitus Facial Image Medical Biometrics Noninvasive Disease Detection Stack Network Traditional Chinese Medicine
DOI10.1016/j.ins.2020.08.074
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science
WOS SubjectComputer Science, Information Systems
WOS IDWOS:000590678800008
PublisherELSEVIER SCIENCE INC, STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
Scopus ID2-s2.0-85091675900
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Citation statistics
Cited Times [WOS]:8   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorZhang,Bob; Wu,Enhua
Affiliation1.PAMI Research Group,Dept. of Computer and Information Science,University of Macau,Macau SAR,China
2.Faculty of Science and Technology,University of Macau,Macau SAR,China
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau;  Faculty of Science and Technology
Recommended Citation
GB/T 7714
Zhang,Qi,Zhou,Jianhang,Zhang,Bob,et al. DsNet: Dual stack network for detecting diabetes mellitus and chronic kidney disease[J]. Information Sciences,2021,547:945-962.
APA Zhang,Qi,Zhou,Jianhang,Zhang,Bob,&Wu,Enhua.(2021).DsNet: Dual stack network for detecting diabetes mellitus and chronic kidney disease.Information Sciences,547,945-962.
MLA Zhang,Qi,et al."DsNet: Dual stack network for detecting diabetes mellitus and chronic kidney disease".Information Sciences 547(2021):945-962.
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