Convolutional Neural Network for Biological Particle Classification
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Solution Overview
Problem
Current methods for classifying biological particles, such as bacteria, are resource-intensive and challenging to interpret, especially when determining antibiotic susceptibility, often requiring fine calibration and extensive computing resources.
Innovation Solution
A method utilizing a pre-trained convolutional neural network for feature extraction and classification, combined with a classifier trained on a database of labeled feature maps, reduces computational demands and improves classification efficiency by representing particles uniformly and aligning them in a standardized manner.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If digital holographic microscopy is used to detect structural modifications rapidly, then measurement time is reduced and sensitivity is improved, but image interpretation becomes difficult and requires fine calibration
Solution Approach 1:
The patent introduces digital image processing algorithms and automated analysis tools as intermediaries between the holographic microscopy system and the user. These processing steps automatically extract and interpret structural modifications from holographic images, eliminating the need for manual calibration and interpretation while preserving the rapid, non-destructive measurement capabilities of the microscopy system
Solution Approach 2:
The patent replaces manual image interpretation (mechanical/human process) with automated digital image processing and analysis algorithms. This substitution transforms the complex interpretive task into an automated computational process, reducing both time loss and interpretation difficulty simultaneously
2Measurement precision
If conventional fluorescence labeling is used to visualize bacteria, then bacterial structures can be revealed, but the process becomes long and complex and requires cytotoxic chemical markers
Solution Approach 1:
The patent extracts and eliminates the complex fluorescence labeling process entirely by using digital holographic microscopy to directly visualize bacterial structures through their intrinsic optical properties. This extraction removes the need for cytotoxic chemical markers and complex labeling procedures while maintaining the ability to reveal bacterial structures and monitor their changes over time
Solution Approach 2:
The patent creates digital copies (holograms) of bacterial structures that can be analyzed without physically altering or labeling the bacteria. These digital representations preserve all structural information while eliminating the need for physical markers, enabling repeated non-destructive observations
Data Source
AI summary
A method for classifying at least one input image representing a target particle in a sample, involves implementing, by data processing of a client, steps of: (b) extracting the characteristic map of the target particle by a convolutional neural network pre-trained on a base of public images; (c) classifying the input image according to the extracted characteristic map.