Abstract
Biomedical imaging has developed as a non-invasive and effective approach for early disease diagnosis and health monitoring. Diabetes mellitus (DM) is a severe metabolic disease with a high global incidence, characterized by the improper secretion of insulin in the pancreas, which results in elevated blood glucose levels. Moreover, it is one of the most life-threatening illnesses, and a prompt prediction of diabetes is of the highest significance in the present scenario. The analytic models, such as fasting plasma glucose, utilized nowadays are considered to be invasive and time-consuming. So, it is highly essential to develop an easy and non-invasive model for diagnosing DM. For the last few years, several analysis techniques that depend on tongue images have been proposed. The diagnosis of DM is a major subdivision of tongue analysis. Recently, numerous deep learning techniques have been developed and shown to be highly efficient in analyzing DM based on tongue images. This paper presents a Deep Feature Engineering with Crayfish Optimization for Accurate Diabetes Disease Detection via Tongue Image Analysis (DFECO-DDTIA) technique in biomedical imaging. The primary goal of the DFECO-DDTIA technique is to develop an accurate diagnostic method for diabetes using advanced tongue imaging techniques. Initially, the DFECO-DDTIA technique utilizes an upgraded weighted median filtering (Up-WMF) method for noise removal, thereby enhancing image quality. For the feature extraction process, the squeeze-and-excitation-DenseNet (SE-DenseNet) method is employed. Furthermore, the DFECO-DDTIA approach implements the temporal convolutional network (TCN) method for classification. To further optimize the model's performance, the Crayfish Optimisation Algorithm (COA) method is employed for hyperparameter tuning, ensuring the selection of optimal parameters to enhance accuracy. To highlight the improved performance of the DFECO-DDTIA approach, a comprehensive experimental analysis is conducted under the Tongue images dataset. The comparison analysis of the DFECO-DDTIA approach revealed a superior accuracy value of 96.91% compared to existing models.