Deep Learning Microorganism Detection Apparatus
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Solution Overview
Problem
Conventional methods for determining microorganism infections, such as Trichomonas, are time-consuming and lack precision, especially in resource-limited areas where medical infrastructure is inadequate and skilled personnel are scarce.
Innovation Solution
A method utilizing a deep learning model to analyze microphotographed images of biological samples, generating analysis information, and providing visual feedback for remote or local reading, allowing for rapid and accurate determination of microorganism presence without replacing existing microscopes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional microscopic inspection methods are used by medical staff, then the inspection can be performed with existing microscopes, but the inspection process is time-consuming and accuracy deteriorates due to repetitive work and lack of skilled personnel
Solution Approach 1:
The patent replaces the mechanical/manual microscopic inspection system with an automated deep learning-based image analysis system. The computing apparatus processes microphotographed images using trained deep learning models to automatically detect microorganisms, substituting the manual mechanical inspection process with an automated computational system that operates faster and with consistent accuracy.
Solution Approach 2:
The patent creates a digital copy of the microscopic inspection process through deep learning models trained on microphotographed images. Instead of relying on human experts to interpret images, the system uses copied and generalized knowledge from training data to automatically identify microorganisms, enabling rapid and accurate detection without requiring skilled personnel.
2Productivity
If deep learning-based automated inspection is implemented, then inspection speed and accuracy improve, but device complexity increases due to computing apparatus and model requirements
Solution Approach 1:
The patent makes the deep learning model universally applicable to different microorganism types and inspection scenarios. The same computing apparatus and deep learning framework can detect various microorganisms by using different trained models, eliminating the need for specialized equipment for each case and reducing overall system complexity through standardized multi-functional architecture.
Solution Approach 2:
The patent performs preliminary training of deep learning models using extensive datasets before actual inspection. This preliminary action prepares the system in advance, so that during actual operation, the computing apparatus can quickly process images without requiring complex real-time decision-making, thereby increasing productivity while keeping the operational system relatively simple.
3Measurement precision
If deep learning models are used for microorganism detection, then detection precision improves, but the system requires computing apparatus and trained models that increase system complexity
Solution Approach 1:
The patent extracts the complex deep learning model training and processing functions into a separate computing apparatus that works in conjunction with the microscope system. This extraction allows the core detection precision to be improved through sophisticated models while keeping the overall system architecture modular and manageable, reducing the perceived complexity by separating concerns between image acquisition and image analysis.
Data Source
AI summary
Provided are a method for determining whether an examinee is infected by microorganism, and a determination apparatus using the same. Specifically, the determination apparatus according to the present invention obtains an microphotographed image of a biological sample of the examinee; receives the obtained microphotographed image and generates analysis information on the microorganism based on a deep learning model of the examinee; visualize the generated analysis information to provide it, so as to perform at least one of (i) a process of supporting a remote reading on whether the microorganism corresponding to the analysis information exists or not, and (ii) a process of supporting a user of the computing apparatus to read whether the microorganism corresponding to the analysis information exists or not; and provides a final result as its result.