Abstract
In recent times, internet of things (IoT) and wireless communication techniques become widely used in healthcare sector. Biomedical image processing is commonly employed to detect the existence of diseases using biomedical images. Tongue diagnosis is an efficient, non-invasive model to perform auxiliary diagnosis any time anywhere that is support the global necessity in the primary healthcare system. Conventionally, medical practitioners investigate the tongue features based on their expert's knowledge comes from experience. In order to eradicate the qualitative aspects, tongue images can be quantitatively examined, offering an effective disease diagnostic process in such a way that the physical harm of the patients can be minimized. Numerous tongue image analysis approaches exist in the literature, it is required to develop automated deep learning (DL) models to diagnose the diseases using tongue image analysis. In this view, this paper designs an automated IoT and synergic deep learning based tongue color image (ASDL-TCI) analysis model for disease diagnosis and classification. The proposed ASDL-TCI model operates on major stages namely data acquisition, pre-processing, feature extraction, classification, and parameter optimization. Primarily, the IoT devices are used to capture the human tongue images and transmitted to the cloud for further analysis. In addition, median filtering based image pre-processing and SDL based feature extraction techniques are employed. Moreover, deep neural network (DNN) based classifier is applied to determine the existence of the diseases. Lastly, enhanced black widow optimization (EBWO) based parameter tuning process takes place to enhance the diagnostic performance. For assessing the effectual performance of the ASDL-TCI model, a set of simulations take place on benchmark tongue images and examined the results under distinct dimensions. The simulation outcome verified the enhanced diagnostic performance of the ASDL-TCI model over the compared methods with the maximum precision, recall, and accuracy of 0.984, 0.973, and 0.983.