## 5 Conclusions and Future Work  
The main challenging aspect of automating tongue analysis for disease diagnosis is quantifying the diagnostic features of the tongue image on par with the expert practitioner's observations. Automating this non-invasive method of diagnosis can facilitate the consultation of a sick individual with medical personnel using a telemedicine network, even from a remote location. On the Indian continent, with its diverse and large population, automatic tongue analysis systems can aid medical personnel to some extent in giving a preliminary diagnosis and initiating immediate treatment without direct physical interaction with the subject. In this study, we introduced a powerful multi-disease detection pipeline for tongue image analysis that uses ensemble learning to combine the predictions of five individually trained models to improve the performance of identifying a diseased tongue. In this particular case, the ensemble method's performance is comparable to that of individual models, showing no notable improvement. A single image of the tongue can be used to predict more than one disease by employing techniques like transfer learning, class weighting for imbalance in different classes, large amounts of real-time data enhancement, and focal loss utilisation in the deep learning architecture. An average accuracy of 90% is achieved with the multi-label classification model for eight disease classes. Two different deep learning models are specifically used, primarily due to their lower resource requirements compared to their counterparts and their potential for Android platform deployment. This study's most significant accomplishment is the successful use of mobile phone images for tongue image analysis, which represents a step towards reducing the cost and expertise of a high-end, sophisticated image-capturing device and strengthens the possibility of developing an Android-based app for easy and quick prognosis. Automation will also be helpful in training aspiring clinicians to use this non-invasive traditional practice and enhance their skills with experience. This opens the opportunity to explore more enhanced models with a larger dataset for performance improvement. In our future work, we aim to build a more robust model with a judicious selection of a group of deep learning architectures for an ensemble model capable of classifying all possible disease labels achievable using tongue analysis from smartphones. This inherently includes the need to compile an exhaustive tongue image dataset for research. **Author Contributions:** [PERSON] has compiled the work after conducting a conducting a literature survey and developing an algorithm that is then trained on the dataset and rigorously analysed by [PERSON], my guide and mentor. [PERSON] critically revised and corrected the first author's rough draft for intellectual content, ultimately approving the submitted version. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Institutional Review Board Statement:** This is an observational study, and hence no ethical approval is required. The individual consent form was obtained from all the subjects and recorded alongside the clinician's diagnosis. The only use of the collected data is for research purposes, never disclosing the identity of any subject. **Informed Consent Statement:** All of the individual participants in the study gave their informed consent. The authors made it clear to them that they would only use the provided images for research purposes and would not disclose any personal information on any platform. **Data Availability Statement:** The data presented in this study are available on request from the corresponding author. **Acknowledgments:** The authors acknowledge the support of medical professionals who contributed to the collection of datasets and the annotation and labelling of the same. We are also thankful to student volunteers of the department of Biomedical Engineering, SGSITS, Indore.