| Autor: |
Sreenivasu SVN; Department of Computer Science and Engineering, Narasaraopeta Engineering College (A), Narasaraopet, India., Santosh Kumar Patra P; Department of Computer Science and Engineering, St. Martin's Engineering College (A), Secunderabad, India., Midasala V; Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, India., Murthy GSN; Department of Computer Science and Engineering, Aditya College of Engineering, Surampalem, India., Janapati KC; Department of Electronics and Communication Engineering, Vardhaman College of Engineering, Hyderabad, India., Swarup Kumar JNVR; Department of Computer Science and Engineering, GITAM School of Technology, GITAM (Deemed to be University), Visakhapatnam, India., Kumar PM; Department of Artificial Intelligence, SAK Informatics, Hyderabad, India. |
| Abstrakt: |
Tongue analysis plays the major role in disease type prediction and classification according to Indian ayurvedic medicine. Traditionally, there is a manual inspection of tongue image by the expert ayurvedic doctor to identify or predict the disease. However, this is time-consuming and even imprecise. Due to the advancements in recent machine learning models, several researchers addressed the disease prediction from tongue image analysis. However, they have failed to provide enough accuracy. In addition, multiclass disease classification with enhanced accuracy is still a challenging task. Therefore, this article focuses on the development of optimized deep q-neural network (DQNN) for disease identification and classification from tongue images, hereafter referred as ODQN-Net. Initially, the multiscale retinex approach is introduced for enhancing the quality of tongue images, which also acts as a noise removal technique. In addition, a local ternary pattern is used to extract the disease-specific and disease-dependent features based on color analysis. Then, the best features are extracted from the available features set using the natural inspired Remora optimization algorithm with reduced computational time. Finally, the DQNN model is used to classify the type of diseases from these pretrained features. The obtained simulation performance on tongue imaging data set proved that the proposed ODQN-Net resulted in superior performance compared with state-of-the-art approaches with 99.17% of accuracy and 99.75% and 99.84% of F1-score and Mathew's correlation coefficient, respectively. |
