Skin Disease Identification Compute System Using Segmented Image Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
There is a need for an accurate and efficient system to identify skin diseases from patient images, as current methods rely heavily on human expertise and can lead to inconsistent diagnoses.
Innovation Solution
A compute system with a skin disease identification mechanism that qualifies patient images, detects skin areas, segments and crops images to analyze suspected skin conditions, and provides a disease identification display with a skin disease indication, image match score, and disease subclass.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple doctors diagnose the same patient, then different diseases may be identified due to varying expertise, but this leads to diagnostic inconsistency and potential errors
Solution Approach 1:
The system segments the diagnostic process into distinct functional modules: image qualification module to validate input images, skin area detection module to locate relevant regions, segmentation module to isolate skin conditions, cropping module to focus on suspected areas, and analysis module to identify diseases. This modular segmentation ensures consistent processing while maintaining system manageability.
Solution Approach 2:
The compute system acts as an intermediary between patient images and diagnostic conclusions. It processes images through standardized algorithms, producing objective disease identification results that assist doctors without replacing human judgment. The system mediates by providing consistent, reproducible analysis across different cases and practitioners.
2Measurement precision
If traditional manual diagnosis methods are used, then doctor experience and training influence diagnosis, but this results in subjective variability and potential missed diagnoses
Solution Approach 1:
The system replaces manual mechanical diagnosis processes with automated computational analysis. Image qualification, skin area detection, segmentation, and disease identification are performed by algorithms rather than human hands and eyes, eliminating subjective variability while maintaining high processing speed and efficiency.
Solution Approach 2:
The system creates standardized digital copies of the diagnostic process through reproducible algorithms. Each image undergoes the same qualification, detection, segmentation, and analysis steps, ensuring consistent measurement precision across different cases without sacrificing processing efficiency.
3Reliability
If skin images are analyzed without standardized processing, then diagnostic results may vary, but implementing comprehensive image processing increases system complexity
Solution Approach 1:
The image processing system is divided into five sequential modules: qualification (validating image quality), detection (locating skin areas), segmentation (isolating skin conditions), cropping (focusing on suspected regions), and analysis (identifying diseases). This segmentation makes the complex processing manageable while ensuring reliable, standardized results at each stage.
Data Source
AI summary
A method of operation of a compute system includes: qualifying a patient image for analyzing a suspected skin condition; detecting a skin area in the patient image; segmenting the skin area into a segmented image including the suspected skin condition; cropping the segmented image to form a cropped image including the suspected skin condition at a center of the cropped image; analyzing the suspected skin condition to identify a skin disease result and a disease subclass from the cropped image; and assembling a disease identification display including the patient image, a skin disease indication, an image match score, and the disease subclass for displaying on a device.