Bronchoscopy Image Typing With Attention-ResNet for EBTB Diagnosis
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Solution Overview
Problem
Endobronchial tuberculosis often lacks specific clinical symptoms and imaging manifestations, leading to frequent misdiagnosis and underdiagnosis, which complicates treatment and increases the risk of tuberculosis transmission.
Innovation Solution
An artificial intelligence-assisted diagnostic system using a ResNet34 framework with multi-head self-attention and depthwise separable convolution to analyze endobronchial endoscopic images, reducing computational burden and improving accuracy to nearly 90% through dual USB foot pedals for independent operation with hospital systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional convolution operations are used in the diagnostic model, then the model can process endobronchial endoscopic images, but the computational burden is excessive and training efficiency is low
Solution Approach 1:
The patent segments the traditional convolution operation into two distinct components: depthwise convolution for spatial feature extraction and pointwise convolution for channel mixing. This segmentation allows each component to be optimized independently, reducing overall computational complexity while maintaining diagnostic accuracy for endobronchial tuberculosis
Solution Approach 2:
The patent changes the parameter configuration by using 1x1 convolution kernels in the pointwise convolution stage instead of traditional larger kernels. This parameter change significantly reduces the number of computations required while preserving the ability to extract meaningful features from endobronchial endoscopic images
2Measurement precision
If the diagnostic model uses complex architectures to improve accuracy, then diagnostic precision increases, but the device complexity and computational requirements increase
Solution Approach 1:
The patent substitutes traditional mechanical convolution operations with a hybrid architecture combining depthwise separable convolution and self-attention mechanisms. This substitution achieves higher diagnostic accuracy for endobronchial tuberculosis while reducing overall model complexity by replacing redundant computational layers with more efficient attention-based feature extraction
3Adaptability or versatility
If multiple systems operate simultaneously for diagnosis and hospital integration, then functionality increases, but system complexity and potential interference increase
Solution Approach 1:
The patent introduces dual USB foot pedals as intermediary control devices that enable independent operation of the AI diagnostic system and hospital bronchoscopy reporting system. These pedals act as mediators, allowing each system to function autonomously without mutual interference while maintaining full functionality for both diagnostic tasks
Data Source
AI summary
Provided are a method and an apparatus for determining endobronchial tuberculosis typing, and a device, and relates to the field of artificial intelligence-assisted diagnosis. The method includes: obtaining a dataset, where the dataset includes an endobronchial endoscopic image sample; constructing an endobronchial tuberculosis diagnostic model, where the endobronchial tuberculosis diagnostic model is an endobronchial tuberculosis diagnostic model that is based on a ResNet34 framework and that incorporates multi-head self-attention and depthwise separable convolution; training the endobronchial tuberculosis diagnostic model based on the dataset; and inputting a bronchoscopy image of a user into a trained endobronchial tuberculosis diagnostic model to obtain the endobronchial tuberculosis typing. According to this application, intelligent diagnosis of endobronchial tuberculosis can be implemented through an artificial intelligence-assisted diagnostic system, so that misdiagnosis and missed diagnosis of endobronchial tuberculosis can be effectively reduced.