Deep Learning Polyp Classification in Colonoscopy Screening
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Solution Overview
Problem
Current colorectal cancer screening methods, such as unaided video colonoscopy using White Light and Narrow Band Imaging, are prone to errors in detecting and misdiagnosing colorectal polyps, leading to inefficiencies and incomplete polyp removal.
Innovation Solution
A deep learning system utilizing a computer vision-aided neural network analyzes colonoscopy images to classify polyps into adenoma, hyperplastic, or serrated types, employing data preprocessing, t-distributed Stochastic Neighbor Embedding for dimensionality reduction, and a multilayer perceptron for robust classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If unaided video colonoscopy using White Light and Narrow Band Imaging is used, then the screening process can be performed, but the detection accuracy and classification reliability of polyps deteriorates
Solution Approach 1:
A deep learning-based image analysis system is introduced as an intermediary between the colonoscopy imaging system and the diagnostic decision. The system processes images captured during colonoscopy and provides automated polyp classification, serving as a mediator that enhances detection accuracy without requiring complex manual analysis by the endoscopist.
Solution Approach 2:
The manual visual assessment and diagnostic decision-making process is replaced with an automated deep learning system. The neural network algorithms substitute for the mechanical process of human eye examination and interpretation, providing consistent and accurate polyp classification across different cases.
2Measurement precision
If manual visual assessment is used for polyp classification, then the system remains simple, but the measurement precision and classification accuracy deteriorates
Solution Approach 1:
The image analysis process is segmented into distinct functional components: image acquisition, feature extraction, deep learning classification, and diagnostic output. This segmentation allows each component to be optimized independently, improving overall measurement precision while managing system complexity through modular architecture.
Solution Approach 2:
The system transforms 2D colonoscopy images into multi-dimensional feature spaces through deep learning algorithms. By converting visual patterns into higher-dimensional representations, the system achieves superior classification accuracy that cannot be obtained through manual 2D assessment alone.
3Loss of information
If comprehensive polyp classification is performed, then the diagnostic information is improved, but the time required for analysis and productivity deteriorates
Solution Approach 1:
The deep learning model performs preliminary classification of polyps during the colonoscopy procedure itself, providing real-time or near-real-time diagnostic information. This preliminary action eliminates the need for subsequent manual review and reduces the time required for complete diagnostic assessment, thereby improving productivity without losing diagnostic information.
Data Source
AI summary
Disclosed are various embodiments of systems and methods of deep learning for colorectal polyp screening and providing a prediction of neoplasticity of a polyp. A video of a colonoscopy procedure can be captured. Frames from the video or images associated with the colonoscopy procedure can be extracted. A model for classifying objects that appear in the frames or the images can be obtained. A classification can be determined for a polyp that appears in at least one of the frames or images based on applying the frames or images to an input layer of the model.