AI Object Classification via ROI Segmentation for Medical Imaging Accuracy
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Solution Overview
Problem
Conventional object classification technologies in medical imaging are prone to misclassification, resulting in low accuracy due to their rough nature, which can lead to incorrect identification of medical images.
Innovation Solution
An AI-based object classification method that involves obtaining a target image, separating the target detection object, inputting it into a feature object prediction model to obtain a feature object segmentation image, and classifying the object based on quantitative feature information to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional object classification technologies are used to directly classify medical images, then the classification process is simple, but the classification accuracy is low due to misclassification
Solution Approach 1:
The patent applies segmentation by dividing the medical image into multiple regions of interest (ROIs) based on detected objects. Each ROI is then processed separately through the classification model, allowing for more precise local analysis while maintaining overall system manageability. This resolves the contradiction by improving classification accuracy through localized processing without overwhelming system complexity.
Solution Approach 2:
The patent introduces an intermediary object detection model that identifies and locates relevant objects before classification. This intermediary step provides precise bounding boxes and ROI extraction, which significantly improves subsequent classification accuracy. The intermediary detection mechanism acts as a bridge between raw image input and final classification, resolving the accuracy-complexity tradeoff.
2Productivity
If the entire medical image is processed for classification, then all image data is considered, but unnecessary data increases processing time and reduces efficiency
Solution Approach 1:
The patent extracts only the relevant regions of interest (ROIs) containing detected objects from the full medical image for classification processing. This extraction principle eliminates unnecessary background and irrelevant areas, significantly reducing processing time and computational resources while maintaining all essential diagnostic information. The ROI extraction directly resolves the efficiency-information loss contradiction.
Solution Approach 2:
The patent segments the medical image into multiple ROI regions based on object detection results. Each segment contains specific objects of interest and is processed independently. This segmentation approach improves productivity by parallelizing processing across multiple smaller regions while preserving all relevant diagnostic information, avoiding the need to process the entire image as a single unit.
3Reliability
If direct classification of medical images is performed, then the process is fast, but misclassification occurs leading to low accuracy
Solution Approach 1:
The patent performs preliminary object detection and ROI extraction before the actual classification task. This preliminary action identifies and isolates relevant objects, providing the classification model with pre-processed, focused input data. By performing this preparatory step, the system achieves high classification reliability through improved input quality while keeping the actual classification phase fast and efficient.
Solution Approach 2:
The patent replaces direct image classification with a two-stage system: object detection followed by ROI-based classification. This substitution transforms the classification process from analyzing entire images to analyzing extracted object regions, significantly improving reliability by focusing computational resources on relevant diagnostic features while maintaining processing speed through efficient model architecture.
Data Source
AI summary
An AI-based object classification method and apparatus, a computer-readable storage medium, and a computer device. The method includes: obtaining a target image to be processed, the target image including a target detection object; separating a target detection object image of the target detection object from the target image; inputting the target detection object image into a feature object prediction model to obtain a feature object segmentation image of a feature object in the target detection object image; obtaining quantitative feature information of the target detection object according to the target detection object image and the feature object segmentation image; and classifying the target detection object image according to the quantitative feature information to obtain category information of the target detection object in the target image.