Endoscopic Lesion Detection Model Selection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Endoscopic lesion detection methods face challenges in balancing accuracy and noise susceptibility, with fixed-number image methods being prone to noise and variable-number image methods potentially missing lesions due to detection delays.
Innovation Solution
An image processing device that selects between a first model for fixed-number images and a second model for variable-number images based on the degree of variation in endoscopic images, using a variation detection mechanism to choose the appropriate model for lesion detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fixed predetermined number of images is used for lesion detection, then detection accuracy is improved, but the system becomes susceptible to noise such as blurring
Solution Approach 1:
The patent dynamically adjusts the number of images used for lesion detection based on the degree of variation between consecutive images. When variation is high, the system uses a variable number of images rather than a fixed predetermined number, allowing the detection process to adapt to changing image conditions and reduce susceptibility to noise while maintaining detection accuracy.
2Object-affected harmful factors
If a variable number of images is used for lesion detection, then noise susceptibility is reduced, but detection delay or miss of lesion may occur
Solution Approach 1:
The patent implements a feedback mechanism where the degree of variation between consecutive images is continuously monitored and used to adjust the number of images for lesion detection. This feedback loop ensures that when variation is low (reducing noise impact), the system can use fewer images to avoid detection delays, while maintaining the ability to detect lesions promptly when variation increases.
3Productivity
If a variable number of images is used for lesion detection, then detection speed is improved, but lesion detection accuracy may deteriorate when there is no substantial change between images
Solution Approach 1:
The patent changes the parameter of image quantity based on the degree of variation between images. When variation is substantial, the system uses a larger number of images to maintain detection accuracy. When variation is minimal, the system uses fewer images to improve detection speed. This dynamic parameter adjustment resolves the contradiction between detection speed and accuracy by adapting to the actual image conditions.
Data Source
AI summary
The image processing device 1X includes an acquisition means 30X, a variation detection means 311X, a selection means 312X, and a lesion detection means 34X. The acquisition means 30X acquires an endoscopic image obtained by photographing an examination target by a photographing unit provided in an endoscope. The variation detection means 311X detects a degree of variation between the endoscopic images. The selection means 312X selects either one of a first model or a second model based on the degree of variation, the first model making an inference regarding a lesion of the examination target based on a predetermined number of the endoscopic images, the second model making an inference regarding the lesion based on a variable number of the endoscopic images. The lesion detection means 34X detects the lesion based on a selection model that is either the first model or the second model selected.