Hyperspectral Small Intestine Hemorrhage Detection Using CNN Analysis
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Solution Overview
Problem
Detecting gastrointestinal hemorrhage in small intestine images using wireless capsule endoscopy is laborious and time-consuming for medical staff due to the large volume of images that need to be manually reviewed.
Innovation Solution
Employ a method using convolutional neural networks to analyze hyperspectral images of the small intestine, performing feature extraction and convolution operations to automatically identify hemorrhage by comparing input images with hyperspectral sample bands, reducing the need for manual image review.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If wireless capsule endoscopy is used to examine the entire small intestine, then complete coverage of the digestive tract is achieved, but the volume of images requiring manual review increases significantly
Solution Approach 1:
The system enables automatic hemorrhage detection through self-service mechanisms where the computational model autonomously analyzes capsule endoscopy images without requiring manual review by medical staff, thus maintaining complete examination coverage while eliminating time-consuming manual analysis
Solution Approach 2:
The patent replaces the mechanical manual review process with an automated computational system that uses hyperspectral image analysis and convolutional neural networks to detect hemorrhage, substituting human labor with automated image processing technology
2Speed
If conventional endoscopy methods are used, then the procedure is relatively quick, but they cannot reach the deep parts of the small intestine
Solution Approach 1:
The wireless capsule endoscopy system provides multi-functionality by combining complete small intestine coverage capability with automated hemorrhage detection, making it suitable for both comprehensive examination and rapid diagnosis without requiring multiple different devices
3Length of moving object
If balloon enteroscopy is used to reach deep small intestine, then deeper penetration is achieved, but the procedure time increases to 1-2 hours
Solution Approach 1:
The patent replaces complex mechanical balloon enteroscopy systems with a simpler wireless capsule endoscopy approach combined with automated image analysis, achieving both deep small intestine examination and rapid hemorrhage detection without the 1-2 hour procedure time required for balloon methods
Data Source
AI summary
The present invention provides a method for detecting tissue hemorrhage with image analysis. A host produces a plurality of hyperspectral image information according to a plurality of reference images. An image extraction unit extracts an input image to the host. The host transforms the input image according to the plurality of hyperspectral image information to produce a hyperspectral input image. The host produces an input image spectrum according to the hyperspectral input image. The host performs a feature operation on the input image spectrum according to a preset cell band corresponding to a surface cell of small intestine for generating a plurality of corresponding feature bands. The host performs at least one convolution operation on the plurality of feature bands according to a plurality of kernels for producing a convolution result. The host matches and compares the convolution result with at least one hyperspectral sample band of at least one hyperspectral sample spectrum of at least one hyperspectral sample image for producing at least one comparison result. Finally the host judges if hemorrhage occurs on the surface layer of small intestine according to the comparison result.