## 2 Related Work
\begin{table}
\begin{tabular}{c c c c} \hline
**Tongue Features/Disease Targeted** & **Dataset** & **Method Employed** & **Reference** \\ \hline
5 tongue body colours, 6 tongue & 1080 images acquired using the DSO1 & k-means clustering algorithm & [34] \\ coating colours & state-of-the art acquisition system & & \\ \hline Diabetes - & The TFDA-1 captured & The auto-encoder algorithm extracts tongue & \\ Texture and Coating features & 732 subject images. & features, and then the k-means algorithm fuses & [35] \\ \hline  & & & \\ The study focuses on tongue area & & extraction models ResNet-50, VGG-16, and & \\ detection, calibration, and & 50 subjects & Inception-V3, alongside LBP for texture & \\ constitution classification. & & features and Colour-Moment for colour & \\  & & features The model was evaluated using the & \\  & & classifiers SVM and Decision Tree. & \\ \hline  & & The Grey Level Co-occurrence Matrix (GLCM), & \\ Colour andTexture features & 702 images, & in conjunction with the LEAD (Multilabel & \\  & & Learning Algorithm) and a & \\  & & threshold-determining algorithm, yields & \\  & & superior results compared to other & \\  & & existing techniques. & \\ \hline  & & GLA (Generalised Lloyd Algorithm) to extract & \\ Multifeature extraction & 268 images, & colour and texture features from the & \\  & & tongue surface. & \\ \hline Seven categories: fissured tongue, & & \\ tooth-marked, statis, spotted, greasy, & 8676 images & The faster R-CNN, a region-based network, & \\ peeled, and rotten coating & & achieved an accuracy of 90.67\%. & [39] \\ \hline considered 11 features on the & & \\ tongue surface. & 482 images & The ResNet-34 architecture has achieved 86\% & \\  & & accuracy for the 11 features identified. & [40] \\ \hline  & & & \\  & & & \\  & & & \\  & & & \\ \hline  & & Features related to gastritis were extracted & \\  & & using a constrained, high-dispersal & \\ Gastritis & 263 gastritis patients, & neural network. & \\  & 48 healthy & Ada Boost, SVM (support vector machine), and & \\  & & MLP (multilayer perceptron classifier) are & [43] \\  & & some examples. & \\ \hline
11 disease categories, plus healthy & There are 936 images, with 78 images & The VGG-19 network’s extracted tongue & \\ tongue images. & for each of the 12 disease categories, including healthy ones. & features, supported by a Random Forest & [44] \\ \hline
12 disease categories, including & 936 images & Designed IoT base automated synergic deep & \\ healthy & For each of the 12 disease categories, & learning tongue colour image analysis model & [45] \\  & there are 78 images. & providing 98.3\% accuracy for disease diagnosis & \\  & & and classification. & \\ \hline  & & Explored the possibility of monitoring health & \\ Iron deficiency & 95 images from the Harvard dataset & status by tongue images using the CNN & \\  & & algorithm, which could be deployed on an & \\  & & Android mobile app. & \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of a literature review of ML-based models for specific tongue features. and assigning a specific single disease label to each tongue sample; they did not focus on multi-label classification. None of the references mentioned aimed to classify multiple disease labels. The aim of this paper is to take a step towards developing a decision support system using deep architecture to predict a holistic tongue diagnosis, not just any particular tongue features or diseases. In the future, it is proposed to further develop it as a mobile app that allows users to consult an expert medical professional and receive a preliminary treatment for immediate relief before conducting detailed investigations.