How DermaCheck AI Works
DermaCheck AI uses advanced deep learning technology to analyze skin lesions and provide preliminary assessments. Our system combines state-of-the-art computer vision with medical image analysis to help identify potential concerns.
The Analysis Process
Image Upload & Preprocessing
When you upload an image, our system first performs digital hair removal using advanced computer vision techniques. This preprocessing step ensures that hair and other artifacts don't interfere with the AI's analysis, similar to how a dermatologist would examine a clean, prepared sample.
Deep Learning Analysis
The preprocessed image is analyzed by a convolutional neural network (CNN) that has been trained on thousands of medical images. The model examines patterns, textures, and visual features that are characteristic of different types of skin lesions.
Biometric Metrics Calculation
Our system calculates clinical metrics based on the ABCDE criteria used by dermatologists:
- Asymmetry Index: Measures how irregular the lesion shape is. Perfectly symmetrical lesions score lower, while irregular shapes score higher.
- Border Irregularity: Analyzes the edge definition and smoothness. Well-defined borders are less concerning than jagged, irregular edges.
- Color Distribution: Examines the dominant pigmentation and color variation within the lesion.
Results & Confidence Score
The AI provides a binary classification (Benign or Malignant) along with a confidence percentage. This score indicates how certain the model is about its prediction based on the patterns it detected.
About Our AI Model
Training Data
Our model was trained on the Skin Cancer Malignant vs Benign dataset from Kaggle, containing over 2,600 training images and 660 validation images. The dataset includes diverse examples of both benign and malignant skin lesions.
Model Performance
The model achieved 83.6% validation accuracy during training. It uses early stopping to prevent overfitting and adaptive learning rate reduction for optimal performance.
Preprocessing Strategy
All images undergo digital hair removal before analysis. This ensures the model focuses on lesion characteristics rather than artifacts, improving both training and inference accuracy.
Architecture
The CNN consists of convolutional layers (32, 64, 128 filters), max pooling layers for dimensionality reduction, and dense layers for final classification. The model uses ReLU activation and sigmoid output for binary classification.
Important Medical Disclaimer
DermaCheck AI is a diagnostic aid tool, not a replacement for professional medical evaluation. The AI analysis is based on visual patterns and should be used for informational purposes only. Always consult with a board-certified dermatologist for:
- Official diagnosis and treatment planning
- Regular skin health check-ups
- Concerns about any skin changes
- Follow-up care and monitoring
The AI model has limitations and may not detect all types of skin conditions. False positives and false negatives are possible. Your health and safety should always be your top priority.