Intelligent Deep Learning Based Disease Diagnosis Using Biomedical Tongue Images

Abstract

The rapid development of biomedical imaging modalities led to its wide application in disease diagnosis. Tongue-based diagnostic procedures are proficient and non-invasive in nature to carry out secondary diagnostic processes ubiquitously. Traditionally, physicians examine the characteristics of tongue prior to decision-making. In this scenario, to get rid of qualitative aspects, tongue images can be quantitatively inspected for which a new disease diagnosis model is proposed. This model can reduce the physical harm made to the patients. Several tongue image analytical methodologies have been proposed earlier. However, there is a need exists to design an intelligent Deep Learning (DL) based disease diagnosis model. With this motivation, the current research article designs an Intelligent DL-based Disease Diagnosis method using Biomedical Tongue Images called IDLDD-BTI model. The proposed IDLDD-BTI model incorporates Fuzzy-based Adaptive Median Filtering (FADM) technique for noise removal process. Besides, SqueezeNet model is employed as a feature extractor in which the hyperparameters of SqueezeNet are tuned using Oppositional Glowworm Swarm Optimization (OGSO) algorithm. At last, Weighted Extreme Learning Machine (WELM) classifier is applied to allocate proper class labels for input tongue color images. The design of OGSO algorithm for SqueezeNet model shows the novelty of the work. To assess the enhanced diagnostic performance of the presented IDLDD-BTI technique, a series of simulations was conducted on benchmark dataset and the results were examined in terms of several measures. The resultant experimental values highlighted the supremacy of IDLDD-BTI model over other state-of-the-art methods.