Internet of Things and Synergic Deep Learning Based Biomedical Tongue Color Image Analysis for Disease Diagnosis and Classification

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