Bhatnagar, V.; Bansod, P.P. Convolution Neural Network Based Multi- Label Disease Detection using Tongue Images. Preprints2024, 2024040565. https://doi.org/10.20944/preprints202404.0565.v1
APA Style
Bhatnagar, V., & Bansod, P.P. (2024). Convolution Neural Network Based Multi- Label Disease Detection using Tongue Images. Preprints. https://doi.org/10.20944/preprints202404.0565.v1
Chicago/Turabian Style
Bhatnagar, V. and Prashant P Bansod. 2024 "Convolution Neural Network Based Multi- Label Disease Detection using Tongue Images" Preprints. https://doi.org/10.20944/preprints202404.0565.v1
Abstract
ABSTRACT
Purpose: Tongue image analysis for disease diagnosis is an ancient traditional non-invasive diagnosis technique widely used by traditional medicine practitioners. Deep Learning based multi-label disease detection models offer tremendous potential to clinical decision support systems, by facilitating preliminary diagnosis.
Methods: In this work, we propose a multi-label disease detection pipeline, where in observation and analysis of tongue images captured and received via smartphones assist in predicting the health status of an individual. All images are voluntarily given by subjects consulting collaborating physicians. Images thus acquired are first and foremost classified either into a diseased or a normal category by a 5-fold cross-validation algorithm using a convolution neural network (MobileNetV2) model for binary classification. Once the diseased label is predicted, the image is used to diagnose multiple diseases using the prediction algorithm based on DenseNet121.
Results: Average accuracy of 93 % was achieved in classifying diseased from normal healthy tongue by detection model with MobileNetV2 architecture. Multilabel disease classification produced more than 90% accurate results for the seven class labels considered.
Conclusion: AI based image analysis shows promising results and an extensive dataset could provide further improvements to this approach. Rather than employing high-cost sophisticated image capturing setup, experimenting with smartphone images opens opportunity to provide preliminary health status to individuals on smartphone prior to further line of treatment and diagnosis.
Public Health and Healthcare, Public Health and Health Services
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
