Wound Analysis System Using CNN Classification
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Solution Overview
Problem
Current wound classification methods are inefficient and inaccurate, relying on human assessment, which can be inconsistent and resource-intensive, and often lack the necessary details required for legal or medical analysis, especially as wounds change over time.
Innovation Solution
A system utilizing Convolutional Neural Networks (CNNs) for image-based wound classification, including pre-processing steps like calibration and normalization, to determine wound types and additional details such as depth and age, providing feedback with confidence parameters and treatment recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If wound classification is performed manually by medical personnel, then accurate medical assessment can be obtained, but resource burden increases and emergency services become saturated
Solution Approach 1:
The patent replaces the manual mechanical assessment process with an automated image analysis system using machine learning algorithms. The system captures wound images and automatically classifies them, substituting human visual inspection with computational analysis to maintain accuracy while improving throughput
Solution Approach 2:
The patent introduces an intermediate image capture and analysis system between the wound and the final classification report. This intermediary system processes images to extract wound characteristics, serving as a bridge between physical wound examination and digital documentation, thereby reducing direct human burden
2Loss of information
If detailed wound analysis is performed manually, then comprehensive wound information can be obtained, but time consumption increases when multiple patients require treatment
Solution Approach 1:
The patent performs preliminary image capture and automated analysis of wound characteristics before final classification and reporting. By pre-processing images to extract key features such as wound area, depth, and tissue type, the system prepares comprehensive data in advance, reducing the time needed for detailed analysis later
Solution Approach 2:
The patent replaces time-consuming manual measurement and documentation with automated image processing algorithms that rapidly analyze wound images to extract detailed characteristics, maintaining information completeness while dramatically reducing analysis time
3Adaptability or versatility
If wound reports are prepared by medical personnel, then medical treatment perspective can be captured, but legal reporting requirements may not be met
Solution Approach 1:
The patent creates a multi-functional reporting system that can generate reports suitable for both medical treatment and legal documentation. The system adapts its output based on the intended use, providing comprehensive wound characterization that satisfies multiple stakeholder requirements simultaneously
Solution Approach 2:
The patent incorporates feedback mechanisms where the system learns from both medical and legal reporting requirements. By analyzing the characteristics needed for different report types, the system adjusts its classification and documentation to meet varying precision requirements for different audiences
4Productivity
If wound analysis is delayed, then medical treatment can proceed immediately, but wound appearance changes and original details are lost
Solution Approach 1:
The patent performs preliminary image capture and automated analysis immediately upon wound presentation, before any treatment alters the wound appearance. By extracting and storing detailed wound characteristics in advance, the system preserves original information while allowing treatment to proceed without delay
Solution Approach 2:
The patent creates digital copies of the wound through high-resolution imaging before treatment begins. These image copies serve as permanent records of the original wound state, allowing detailed analysis to occur later without losing information about the initial presentation
Data Source
AI summary
A system for classifying wounds is configured to receive an image of a wound, to determine a classification of the wound, and to provide feedback to a user based on the classification of the wound.