AI Wound Segmentation Using Deep Learning for Precise Area Measurement

The patent replaces manual mechanical measurement methods with an automated image processing and deep learning-based measurement system. The system uses smartphone captured images processed through convolutional neural networks to automatically segment wound regions and calculate area measurements, eliminating the need for manual ruler placement and visual estimation while significantly improving measurement precision.

The patent creates a digital copy of the wound through smartphone imaging and uses this digital representation for analysis instead of direct physical measurement. The deep learning model processes the image copy to extract wound boundaries and calculate area, providing accurate measurements without requiring direct physical contact or manual intervention with the actual wound.

The patent replaces time-consuming manual measurement processes with automated image processing and deep learning algorithms. The system automatically captures wound images using smartphone cameras, processes them through pre-trained neural network models, and generates area measurements instantaneously, dramatically improving productivity compared to manual methods that require clinician time and expertise.

The patent employs pre-trained deep learning models that have been previously trained on large datasets of wound images. These pre-trained models can be directly applied to new wound images without requiring retraining, enabling rapid automated analysis and measurement while reducing the complexity of model deployment and maintenance.

The patent develops a universal deep learning framework that can handle various types of wounds and imaging conditions through a single standardized system. The encoder-decoder architecture with attention mechanisms provides a multi-functional solution that adapts to different wound characteristics, skin tones, and lighting conditions, reducing the need for multiple specialized measurement tools and simplifying implementation across diverse clinical scenarios.

The patent introduces an intermediary processing layer that bridges the gap between raw smartphone images and accurate wound measurements. The system uses intermediate representations including segmentation masks, probability maps, and feature extractions from the deep learning model to translate complex image data into precise area measurements, making the measurement process more robust and easier to implement.

The patent segments the deep learning processing into distinct functional components: an encoder for feature extraction, an attention mechanism for focusing on relevant regions, and a decoder for generating segmentation masks. This segmentation allows the system to process only the most relevant image regions with high computational resources while using lighter processing for other areas, reducing overall energy consumption while maintaining high measurement precision.

The patent applies attention mechanisms that focus computational resources partially on the most critical regions of the wound image rather than processing the entire image uniformly. The attention mechanism identifies and emphasizes key wound boundaries and features, applying intensive processing only where needed, thereby achieving high measurement accuracy with reduced overall computational energy consumption.