CNN-Transformer Scalp Analysis for Automated Dandruff Severity Rating
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Solution Overview
Problem
Current methods for analyzing dandruff severity require professional training and are costly, making it inefficient for quickly assessing and determining the severity of dandruff, especially for those without extensive expertise.
Innovation Solution
A smart dandruff analysis system utilizing a combination of a Convolutional Neural Network (CNN) and Transformer models, along with a scalp tester and semi-supervised learning, to automatically detect and classify dandruff severity through image analysis, reducing the need for professional operators and lowering costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If professional operators are used to determine dandruff conditions, then accuracy of assessment is improved, but labor cost and training time increase
Solution Approach 1:
The system enables self-service dandruff assessment through automated image analysis. The CNN and Transformer models process scalp images automatically without requiring professional operators, allowing users to assess their own dandruff conditions by simply capturing images with a mobile device.
Solution Approach 2:
The patent replaces the mechanical system of manual professional assessment with an automated AI-based image analysis system. The convolutional neural network and transformer model substitute for human experts, performing dandruff severity evaluation through computational algorithms that process visual data.
2Measurement precision
If manual professional assessment is used, then diagnostic accuracy is improved, but time consumption increases
Solution Approach 1:
The system enables continuous and rapid assessment by processing multiple scalp images through the neural network pipeline without interruption. The automated workflow allows for quick sequential evaluation of different scalp regions, maintaining diagnostic accuracy while significantly reducing total assessment time compared to manual methods.
3Reliability
If professional training is provided to operators, then assessment quality is improved, but cost and time investment increase
Solution Approach 1:
The system creates a digital copy of expert assessment capability through trained neural network models. The CNN and Transformer architectures learn from labeled training data to replicate the diagnostic reasoning of professional operators, encoding expert knowledge into algorithms that can be deployed without requiring actual expert operators.
Data Source
AI summary
A smart dandruff analysis system and method are provided for analyzing a severity of a subject's dandruff, and the smart dandruff analysis system has operation module, first neural network module, second neural network module, and classification module. The operation module receives a scalp area image of the subject and transforms the scalp area image into a first feature map. The first neural network module, a Convolutional Neural Network model, electrically connects with the operation module for receiving and transforming the scalp area image into a second feature map. The second neural network module, a Transformer model, electrically connecting with the first neural network module for receiving and transforming the second feature map into a third feature map. The classification module electrically connects with the second neural network module for receiving the third feature map and outputting a rating, wherein the rating is to determine the severity of the subject's dandruff.