# Convolutional herbal prescription building method from multi-scale facial features

## TL;DR

This paper introduces a multi-scale convolutional neural network approach to predict Traditional Chinese Medicine prescriptions from facial images, leveraging facial features at different granularities.

## Contribution

It proposes a novel multi-scale CNN model that captures facial features at organ, local, and whole-face levels to generate TCM prescriptions from face images.

## Key findings

- Multi-scale CNNs outperform single-scale models.
- Facial features contain significant information for TCM prescription prediction.
- The method demonstrates promising results in mining face-prescription relationships.

## Abstract

In Traditional Chinese Medicine (TCM), facial features are important basis for diagnosis and treatment. A doctor of TCM can prescribe according to a patient's physical indicators such as face, tongue, voice, symptoms, pulse. Previous works analyze and generate prescription according to symptoms. However, research work to mine the association between facial features and prescriptions has not been found for the time being. In this work, we try to use deep learning methods to mine the relationship between the patient's face and herbal prescriptions (TCM prescriptions), and propose to construct convolutional neural networks that generate TCM prescriptions according to the patient's face image. It is a novel and challenging job. In order to mine features from different granularities of faces, we design a multi-scale convolutional neural network based on three-grained face, which mines the patient's face information from the organs, local regions, and the entire face. Our experiments show that convolutional neural networks can learn relevant information from face to prescribe, and the multi-scale convolutional neural networks based on three-grained face perform better.