AI Brain Hemorrhage Classification Using Segmented CNN Processing
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Solution Overview
Problem
Deep learning networks are not sufficiently fast or accurate for clinical use in processing medical images, particularly in distinguishing between regions in images, which hampers their reliability in medical settings for brain hemorrhage classification.
Innovation Solution
A system utilizing a computer with a hardware processor and memory, implementing a trained convolutional neural network (CNN) to process medical images, generate class activation maps, label regions associated with brain hemorrhages, and provide confidence values for each labeled region, facilitating rapid and accurate identification of brain hemorrhages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning networks are used for medical image processing, then diagnostic accuracy is improved, but processing speed is insufficient for clinical needs
Solution Approach 1:
The patent segments the medical image processing task into multiple stages: preprocessing, feature extraction, classification, and post-processing. The deep learning network is applied selectively to critical regions identified in earlier stages, rather than processing the entire image at full resolution, thus maintaining accuracy while reducing overall processing time.
Solution Approach 2:
The patent performs preliminary processing steps including image preprocessing, region-of-interest detection, and initial feature extraction before applying the computationally intensive deep learning classification. This preliminary action prepares the data in advance, allowing the main classification algorithm to work more efficiently on pre-processed inputs.
2Measurement precision
If deep learning networks are used for medical image processing, then diagnostic accuracy is improved, but the system may not be sufficiently accurate to distinguish between regions in an image
Solution Approach 1:
The patent applies different processing strategies to different regions of the medical image. Critical regions requiring high accuracy undergo more intensive analysis with higher resolution processing, while non-critical regions receive standard processing. This local quality approach ensures that the system focuses computational resources on distinguishing between critical regions with the necessary precision.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system's confidence in its classifications is evaluated and used to determine whether additional verification is needed. Low-confidence predictions trigger re-analysis or referral to expert review, while high-confidence predictions are accepted automatically. This feedback loop continuously improves reliability by learning from past performance.
3Productivity
If automated AI processing is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent designs a multi-functional processing system that can handle multiple types of medical images and classification tasks using a unified framework. The same core architecture adapts to different image modalities and diagnostic requirements through configurable parameters and training data, reducing the need for separate specialized systems for each application.
Data Source
AI summary
Systems and methods for rapid, accurate, fully-automated, brain hemorrhage deep learning (DL) based assessment tools are provided, to assist clinicians in the detection & characterization of hemorrhages or bleeds. Images may be acquired from a subject using an imaging source, and preprocessed to cleanup, reformat, and perform any needed interpolation prior to being analyzed by an artificial intelligence network, such as a convolutional neural network (CNN). The artificial intelligence network identifies and labels regions of interest in the image, such as identifying any hemorrhages or bleeds. An output for a user may also include a confidence value associated with the identification.