Organ Classification System Using Binary Search
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Solution Overview
Problem
Conventional methods for classifying internal organ images, such as those of the digestive system, require manual marking by specialists, which is time-consuming and labor-intensive.
Innovation Solution
An organ classification system utilizing deep learning to identify and classify organ images in real-time by searching for frames where specific organs appear in input images, employing a binary search method and storing location information for efficient classification and display.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual marking by specialists is used to classify organ images, then classification accuracy can be maintained, but the time and labor required increases significantly
Solution Approach 1:
The system performs preliminary learning by training a deep learning model with organ image data before actual classification. The model learns to identify organ types and their characteristics in advance, so that during runtime, classification can be performed automatically without requiring specialist intervention for each image, thus reducing time while maintaining accuracy
Solution Approach 2:
The patent replaces the mechanical manual marking process by specialists with an automated image processing system using deep learning. The system automatically detects and classifies organ images by analyzing pixel patterns and features, substituting human manual work with computational algorithms that can process images rapidly and consistently
2Productivity
If deep learning is used to automatically classify organ images, then time and labor are reduced, but system complexity increases
Solution Approach 1:
The deep learning model is designed to handle multiple organ types and classification tasks within a single unified system. The model can classify different organs (esophagus, stomach, small intestine, large intestine) using the same architecture and processing pipeline, reducing overall system complexity compared to having separate specialized systems for each organ type
Solution Approach 2:
The system uses learned representations and patterns from training data to classify new images. Instead of requiring complex rule-based systems for each organ type, the model learns generalizable features during training that can be applied universally to classify various organ images, simplifying the classification logic
3Speed
If binary search method is used to search for organ frames, then searching speed is enhanced, but the algorithm complexity increases
Solution Approach 1:
The binary search method divides the search space of frames into two halves at each step, systematically narrowing down the location of the target organ frame. This segmentation of the search process into logarithmic steps achieves O(log n) time complexity, providing rapid search performance while maintaining a simple and elegant algorithmic structure
Data Source
AI summary
The present invention relates to an organ classification system, and the system for classifying the type of an organ may comprise: a search unit for searching an input image for a frame where an image regarding an organ starts, on the basis of a plurality of organ images pre-learned according to types of organs; an obtainment unit for obtaining an image of a found frame from the input image; and a storage unit for storing location information of the found frame in the input image.