Automatic X-Ray Diagnosis Through Neural Feature Matching
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Solution Overview
Problem
Radiologists face challenges in efficiently processing large volumes of X-ray studies, leading to delayed diagnoses and potential misdiagnoses due to the complexity of diseases and limitations of imaging devices, which can be harmful to patients.
Innovation Solution
An artificial neural network model is applied to X-ray images to extract feature vectors, comparing them with annotated images to determine similarity levels and assign disease classifications, utilizing preprocessing techniques like image portioning and flipping to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If radiologists manually process X-ray studies, then diagnostic accuracy can be maintained through expert analysis, but diagnostic efficiency deteriorates due to large workload volume causing delays
Solution Approach 1:
An artificial neural network model serves as an intermediary between the X-ray images and the radiologist. The model automatically extracts feature vectors from images, compares them with annotated images in a database, and provides preliminary disease classifications. This intermediary system handles the time-consuming manual comparison work, allowing radiologists to focus on final diagnosis while maintaining high accuracy through the model's ability to detect subtle patterns.
2Productivity
If the volume of X-ray studies increases, then more patients can be served, but diagnostic accuracy deteriorates due to the limits of imaging devices and anatomic complexity causing missed subtleties
Solution Approach 1:
The patent replaces the mechanical system of manual image analysis with an automated computational system. The artificial neural network model automatically processes X-ray images by extracting feature vectors, comparing them with a database of annotated images, and identifying disease patterns. This substitution enables the system to maintain high diagnostic accuracy even when processing large volumes of images, as the automated system does not suffer from human fatigue or attention limitations.
3Measurement precision
If manual analysis of X-ray images is performed, then detailed examination is possible, but time consumption increases due to the complexity of diseases and imaging limitations
Solution Approach 1:
The system performs preliminary analysis by automatically extracting feature vectors from X-ray images and comparing them with a pre-built database of annotated images. This preliminary action includes preprocessing steps such as dividing images into portions and applying enhancement techniques. The model generates preliminary disease classifications before radiologist review, significantly reducing the time required for detailed examination while maintaining precision through the automated feature extraction and comparison process.
Data Source
AI summary
Various systems, methods, and non-transitory computer readable mediums for diagnosing a query X-ray image are disclosed. Example embodiments relate to operating the system to apply an artificial neural network model to the query X-ray image to extract a query feature vector representative of image characteristics of the query X-ray image; compare the query feature vector with one or more annotated feature vectors associated with respective one or more annotated X-ray images stored in an annotated image database to determine a similarity level between the query X-ray image and each annotated image of the one or more annotated images; associate the query X-ray image with a set of annotated images based at least on the similarity level and a similarity threshold; and assign the query X-ray image with a disease classification based at least on the disease classification of one or more annotated images of the set of annotated images.