AI Systems for Skin Analysis

A system for monitoring skin health at home and providing personalized skin care advice by analyzing interim images and compressing the results into a synoptic representation of skin characteristics. The system uses machine learning techniques to analyze user-captured skin images and extract relevant information about skin health. This compressed representation is then transmitted to a dermatologist who can quickly understand the skin concerns and provide targeted advice without needing to review the full image dataset.

Source

Skin health visualization method, device and equipment based on big data analysis to improve the accuracy of skin health analysis and visualization by using pixel-level classification. The method involves enhancing skin images, extracting detailed features, calculating a global dependency graph, fusing detailed and semantic features, categorizing pixels based on skin health factors, and visualizing skin health using the categorized pixels. This allows pixel-level skin health assessment and visualization rather than just overall skin health.

Source

Diagnosing skin conditions using 2D images captured with a smartphone camera. The system involves processing the captured images to diagnose wrinkles, pores, skin tone, and pigmentation. It uses machine learning to analyze the images and derive diagnosis results. The image processing steps include resizing, brightness equalization, segmentation, feature extraction, binarization, polarization, and score calculation. The system detects oiliness using brightness differences, keratin using lack of pigmentation, and pigmentation using semantic segmentation.

Source

Skin diagnosis system that accurately diagnoses skin conditions using deep learning and compensated detection of multiple skin diseases. The system involves training separate deep learning models for different skin diseases. To improve diagnosis accuracy, the models learn by intersecting areas detected by each model on real images and extracting areas where the intersection exceeds a threshold. This provides valid areas for learning. By compensating for each disease's area detection, the models can better diagnose the multiple skin diseases simultaneously.

Source

System for early detection and classification of skin diseases using deep learning. The system involves capturing real-time images of a subject's skin using a camera, preprocessing the images, augmenting them, applying deep learning models, and using performance metrics to classify skin diseases. The preprocessing steps include resizing and training. Augmentation involves techniques like scaling, cropping, rotation, and transitions to create additional training data. The deep learning models are trained using labeled skin disease datasets. The system leverages custom layers and unique activation functions for improved accuracy. It compares captured images to public databases for diagnosis.

Source

Using deep learning to automatically detect and classify skin conditions from images. The method involves extracting features from images of skin lesions and combining them with textual input like symptom descriptions to generate combined vectors. These vectors are compared with stored vectors associated with known skin disorders to identify and classify the condition. The system uses a trained learning module with a repository of labeled images to classify skin disorders.

Source

Objectively determining the state of living tissue like hair or skin using machine learning. The method involves acquiring an image of the tissue with a demarcated region surrounded by a boundary. Features like size and shape of the region are calculated. A machine learning model is used to recognize the region and another model determines the tissue state based on the feature values. This provides objective tissue condition assessment compared to subjective visual diagnosis.

Source

Analyzing skin diseases using AI and machine learning techniques to improve accuracy compared to visual diagnosis by human dermatologists. The method involves using a trained neural network model to analyze skin images, considering factors like past medical history, environmental trends, and changes between multiple images. The model learns connectivity between layers and neurons in the neural network. The analysis results are outputted to provide skin disease diagnosis based on the images.

Source

Skin condition warning system for auxiliary follow-up consultation that enables real-time monitoring of skin conditions, dynamically adjusting treatment regimens, and improving follow-up efficiency for dermatology patients. The system uses image processing techniques to analyze skin conditions from patient-submitted images. It extracts features like acne scars and cyst size, calculates severity scores, and provides recommendations to doctors for targeted treatment. By leveraging patient-submitted images, the system allows remote monitoring and follow-up without requiring in-person visits.

Source

AI-based skin disease diagnosis system that uses deep learning to accurately identify skin conditions. The system acquires images from users and compares them against learned skin disease classes using CNNs. It provides users with the identified disease and allows re-learning from new images. The system uses a convolutional neural network (CNN) to classify skin diseases based on learned classes. It compares user-provided skin images against the learned classes to identify the disease. The system also allows re-learning from new images.

Source

AI-based skin disease diagnosis device that uses convolutional neural networks (CNNs) to learn and diagnose skin diseases. It acquires skin disease images, classifies them using CNNs, learns and stores the classes, then diagnoses new images using the learned classes to determine if they are skin cancer, skin pigment disease, infectious disease, or normal skin. The device can run on smart devices after learning. By learning and storing skin disease classes, it improves accuracy compared to just applying CNNs.

Source

A neural network system for accurately diagnosing skin conditions using images and metadata. The system receives images of an affected skin area along with metadata about the patient and symptoms. It processes the images using a separate neural network to classify the skin conditions. An input array is created containing the metadata and the image classification confidence values. This array is then fed into a primary neural network to diagnose the skin condition. This allows using limited images by leveraging metadata and trained image classifiers.

Source

Analyzing pixel data of an image of a skin area of a user for determining skin roughness using AI. The method involves training a skin roughness model with images of individuals' skin to output roughness values. When a user's skin image is analyzed, the model determines the user's skin roughness and generates recommendations to address identified rough areas.

Source

Determining suitability of a skin analysis image before actually analyzing the skin to provide consistent and accurate skin analysis results. The method involves checking if the image is appropriate for skin analysis by analyzing factors like orientation, blurriness, brightness, local lighting, and makeup level. If the image fails certain criteria, it indicates unsuitable conditions like poor focus, brightness, or lighting. The server instructs re-photographing for better images. This prevents analyzing inappropriate images that can lead to wrong skin analysis results.

Source

Method for diagnosing skin conditions using artificial intelligence that involves preprocessing skin images for diagnosis. The method involves training an AI model to recognize groups of skin images based on features like hair, patches, and backgrounds. Then, when diagnosing new skin images, the model groups them based on these features and applies appropriate preprocessing for each group. This allows more accurate diagnosis from varied skin images.

Source

Analyzing skin features to help people understand how others perceive their skin issues. It involves detecting skin features in images, determining how much the person interacts with each feature (e.g., looking, touching), and calculating a severity score based on that interaction. This score indicates how concerned the person is about the feature. Additionally, a social perception score is calculated for each feature based on how others typically view similar features. The severity score can then be compared to the social perception score to provide a more accurate understanding of how visible the feature is to others. This helps people better assess whether their concern about a skin issue is justified or excessive.

Source

Skin type determination method that allows for improvement in the accuracy and integrity of skin score data by training an artificial intelligence module using labeled learning data. The method includes receiving, by an electronic device, a first image of a user's skin, and generating a second image by pre-processing the first image.

Source

Determining skin type and melanin index using images to enable personalized skin care and treatment. The method involves analyzing skin images to determine skin type and melanin index based on characteristics of the pigment network structure. It uses an algorithm trained on images annotated with skin type and melanin index to analyze new images. This allows assessing skin without contact devices. By identifying skin properties like melanin distribution and pigment network density, it provides personalized skin care without invasive devices. The method involves normalizing images to eliminate color and using polarized light to improve skin depth in images.

Source

Using machine learning models to diagnose skin diseases. The method involves obtaining images of a patient's skin, generating embeddings for each image using a first portion of a machine-learned model, combining the embeddings into a unified representation, and using a second portion of the model to classify the patient's skin condition based on the unified representation. The model can also process additional patient metadata alongside the images to improve diagnosis accuracy.

Source

Detecting skin conditions in a human subject using computer vision and machine learning to provide more complete and precise skin condition detection compared to existing methods. The system extracts significant aspects like pore size, wrinkles, acne, etc. from images, generates normalized skin characteristic data, and predicts skin conditions like texture, tone, translucency. The predictions are displayed to the user. The method involves extracting object/significant aspect data, generating normalized skin characteristic data, and predicting skin conditions using machine learning models.

Source