Diabetic patients often face various health complications, and one of the lesser-known areas of research is in tongue image analysis. Believe it or not, the condition of a diabetic patient’s tongue can reveal a lot about their overall health. In this post, we’re going to dive into a fascinating project that aims to develop a multi-task tongue image feature prediction model for diabetic patients.
The project involves a dataset of 600 diabetic patients, each with 2 tongue images and 15 tongue feature annotations. The goal is to design a model that can take these 2 images as input and simultaneously predict all 15 features, each with multiple possible classes. Sounds like a challenge, right?
The current approach involves using a ResNet backbone with 16 classification heads and Focal Loss, but the results are disappointing, with an accuracy of only 60.38%. It’s clear that there’s room for improvement, and that’s where we need your help.
If you’re familiar with deep learning and have experience with image classification tasks, we’d love to hear your thoughts on how to improve this model. Perhaps there are other architectures or techniques that could be used to boost accuracy?
Let’s work together to create a more effective model for diabetic patients. The potential benefits are immense, and it’s an exciting area of research that could make a real difference in people’s lives.