Passive Magnetic Localization Based on Connotative Pre-Calibration for Tongue-Machine-Interface

Shen, Hui-Min; Ge, Di; Lian, Chong; Yang, Yue

doi:10.1109/access.2019.2962710

Cited by 4 publications

(1 citation statement)

References 32 publications

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“…Srividhya and Muthukumaravel performed tongue shape, colour, size and texture based disease classification based on self-organizing map Kohonen Classifier technique 24 . Shen et al developed a tongue–machine–interface based on passive magnetic localization strategy 25 . Thirunavukkarasu et al made diabetes classification based on the thermal variations of tongue, the RGB color histogram was employed to extract the features, and the classification was made based on convolutional neural network (CNN) technique 26 .…”

Section: Existing Work Of Literaturementioning

confidence: 99%

Panoramic tongue imaging and deep convolutional machine learning model for diabetes diagnosis in humans

Balasubramaniyan¹,

Jeyakumar

Nachimuthu

2022

Sci Rep

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Diabetes is a serious metabolic disorder with high rate of prevalence worldwide; the disease has the characteristics of improper secretion of insulin in pancreas that results in high glucose level in blood. The disease is also associated with other complications such as cardiovascular disease, retinopathy, neuropathy and nephropathy. The development of computer aided decision support system is inevitable field of research for disease diagnosis that will assist clinicians for the early prognosis of diabetes and to facilitate necessary treatment at the earliest. In this research study, a Traditional Chinese Medicine based diabetes diagnosis is presented based on analyzing the extracted features of panoramic tongue images such as color, texture, shape, tooth markings and fur. The feature extraction is done by Convolutional Neural Network (CNN)—ResNet 50 architecture, and the classification is performed by the proposed Deep Radial Basis Function Neural Network (RBFNN) algorithm based on auto encoder learning mechanism. The proposed model is simulated in MATLAB environment and evaluated with performance metrics—accuracy, precision, sensitivity, specificity, F1 score, error rate, and receiver operating characteristics (ROC). On comparing with existing models, the proposed CNN based Deep RBFNN machine learning classifier model outperformed with better classification performance and proving its effectiveness.

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Section: Existing Work Of Literaturementioning

confidence: 99%