Artificial intelligence-based scalp image diagnostic analysis system using big data, and product recommendation system using the same

1. An artificial intelligence-based scalp image diagnostic analysis system using big data, the system comprising:

a main processor ( 3 ) configured to: receive, from a diagnostician, information about a customer's history taken by the diagnostician by asking about the customer's history, and a scalp image obtained by any one of a scalp diagnosis device and a terminal, through API (RESTful) ( 2 ) as a cloud service; conduct a diagnosis by a self-diagnosis algorithm with respect to the received history-taking information; and transmit the received scalp image to an artificial-intelligence processor ( 5 ), for performing a scalp diagnosis;

the artificial-intelligence processor ( 5 ) configured to perform an artificial intelligence (AI) analysis to label the scalp image received from the main processor ( 3 ) with all or some of diagnosis items by use of data accumulated in database ( 4 );

wherein the artificial-intelligence processor ( 5 ) learns about the scalp by the artificial intelligence (AI) analysis using information of big data and collect learning data as a deep learning stage, labels the collected learning data, conducts learning and verification to label the collected data with learning data and test data, and derives an inference model (CNN: Convolutional Neural Network),

wherein the deep learning conducts scalp labelling (CNN: object recognition) through retraining by use of TensorFlow and an Inception V3 model, whereby the scalp is labelled with all or some of the diagnosis items,

a scalp diagnosis AI algorithm ( 6 ) configured to: receive, from the artificial-intelligence processor ( 5 ), information labeled with all or some of the diagnosis items; conduct a specific precision diagnosis by performing learning and interpretation by a deep learning algorithm, and derive a final diagnosis result; and

the database ( 4 ) accumulating therein scalp measurement, diagnosis, and recommendation data, which are provided to the main processor, thereby enabling self-scalp a diagnosis and recommendation service to be performed,

wherein the artificial-intelligence processor counts multiple numbers of hair follicle groups and multiple number of follicles within each group based on a microscopic image of a sample from a human scalp, and

wherein the diagnosis items include the scalp types which are dry, sensitive, inflammatory, with hair loss, good, oily, scurfy, and seborrhoeic.

2. The system of claim 1 , wherein the scalp diagnosis AI algorithm ( 6 ) infers an image through additional retraining from a scalp image set drawn by use of the inception V3 model as the deep learning algorithm, and derives a final diagnosis result through a precision diagnosis based on information labelled with each diagnosis item.

3. An artificial intelligence-based scalp image diagnostic analysis system using big data, the system comprising:

a main processor ( 3 ) configured to: receive, from a diagnostician, information about a customer's history taken by the diagnostician by asking about the customer's history, and a scalp image obtained by any one of a scalp diagnosis device and a terminal ( 1 ), through API (RESTful) ( 2 ) as a cloud service; conduct a diagnosis by a self-diagnosis algorithm with respect to the received information; label the received scalp image by an artificial intelligence (AI) analysis with all or some of diagnosis items by use of information of big data accumulated in database; extract a precision diagnosis from the labelled information by a scalp diagnosis AI algorithm; and transmit a diagnosis result therefrom and a diagnosis based on the information taken about the customer's history in real time back to a terminal of the diagnostician through the API, together with a recommended product customized by suitable prescription;

the artificial-intelligence processor ( 5 ) configured to perform an AI analysis and recommendation service to label the scalp image received from the main processor ( 3 ) with all or some of diagnosis items by use of the data accumulated in the database ( 4 );

wherein the artificial-intelligence processor ( 5 ) learns about the scalp by the artificial intelligence (AI) analysis using information of big data and collect learning data as a deep learning stage, labels the collected learning data, conducts learning and verification to label the collected data with learning data and test data, and derives an inference model (CNN: Convolutional Neural Network),

wherein the deep learning conducts scalp labelling (CNN: object recognition) through retraining by use of TensorFlow and an Inception V3 model, whereby the scalp is labelled with all or some of the diagnosis items,

a scalp diagnosis AI algorithm ( 6 ) configured to: receive, from the artificial-intelligence processor ( 5 ), information labeled with all or some of the diagnosis items; conduct a specific precision diagnosis by a deep learning algorithm, and derive a final diagnosis result; and

the database ( 4 ) accumulating therein scalp measurement, diagnosis, and recommendation data, which are provided to the main processor, thereby enabling learning and interpretation,

wherein the artificial-intelligence processor counts multiple numbers of hair follicle groups and multiple number of follicles within each group based on a microscopic image of a sample from a human scalp, and

wherein the diagnosis items include the scalp types which are dry, sensitive, inflammatory, with hair loss, good, oily, scurfy, and seborrhoeic.

4. The system of claim 3 , wherein the customer receives recommendation by the diagnostician (user) of a product customized for the customer by various algorithms according to the final analysis result about the customer's scalp.

5. An artificial intelligence-based scalp image diagnostic analysis system using big data, the system comprising:

an artificial-intelligence processor ( 5 - 1 ) configured to: receive, from a diagnostician, a scalp image obtained by any one of a scalp diagnosis device and a terminal, through API (RESTful) ( 2 ) as a cloud service; and perform an artificial intelligence (AI) analysis with respect to the received history-taking information to label the received information with all or some of diagnosis items;

wherein the artificial-intelligence processor ( 5 - 1 ) learns about the scalp by the artificial intelligence (AI) analysis using information of big data and collect learning data as a deep learning stage, labels the collected learning data, conducts learning and verification to label the collected data with learning data and test data, and derives an inference model (CNN: Convolutional Neural Network),

wherein the deep learning conducts scalp labelling (CNN: object recognition) through retraining by use of TensorFlow and an Inception V3 model, whereby the scalp is labelled with all or some of the diagnosis items,

a scalp diagnosis AI algorithm ( 6 - 1 ) configured to: receive, from the artificial-intelligence processor ( 5 - 1 ), information labeled with all or some of the diagnosis items; conduct a specific precision diagnosis by performing learning and interpretation by use of information of big data by a deep learning algorithm; and derive a final diagnosis result,

wherein the artificial-intelligence processor counts multiple numbers of hair follicle groups and multiple number of follicles within each group based on a microscopic image of a sample from a human scalp, and

wherein the diagnosis items include the scalp types which are dry, sensitive, inflammatory, with hair loss, good, oily, scurfy, and seborrhoeic.

6. The system of claim 5 , wherein the scalp diagnosis AI algorithm ( 6 - 1 ) infers an image by additional retraining a scalp image set drawn by use of the inception V3 model as the deep learning algorithm, and derives a final diagnosis result through a precision diagnosis based on information labelled with each diagnosis item.