Brain Tumor MRI Classification Using Preprocessed Image Pathways
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Solution Overview
Problem
Current brain tumor diagnosis methods, such as biopsy and extensive imaging, are invasive, time-consuming, and subjective, necessitating a safer and faster way to accurately diagnose brain tumors based on imaging to guide effective treatment planning.
Innovation Solution
A brain tumor types distinguish system utilizing a server computing device with modules for image pre-processing, data comparison, and statistical analysis to rapidly identify brain tumor types from MRI images using a modified VGG16 model and data augmentation techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If biopsy is used for brain tumor diagnosis, then diagnostic accuracy is improved, but invasiveness and time consumption increase
Solution Approach 1:
The patent replaces the mechanical invasive biopsy system with an automated image analysis system using deep learning algorithms. The system processes MRI images through convolutional neural networks to automatically identify and classify brain tumors, eliminating the need for physical tissue sampling while maintaining diagnostic accuracy.
Solution Approach 2:
The patent introduces computer-aided image analysis as an intermediary between imaging and diagnosis. The system uses processed MRI images as intermediaries to convey tumor information to clinicians, allowing indirect observation and classification without direct tissue intervention.
2Measurement precision
If biopsy is used for brain tumor diagnosis, then diagnostic accuracy is improved, but time consumption increases
Solution Approach 1:
The patent performs preliminary automated analysis of MRI images before clinical decision-making. The deep learning system pre-processes and classifies tumors in advance, providing diagnostic recommendations that guide subsequent clinical actions, thereby reducing overall diagnostic time while maintaining accuracy.
Solution Approach 2:
The patent changes the diagnostic parameter from tissue-based histological analysis to image-based radiological features. By transforming the diagnostic approach from examining physical tissue samples to analyzing imaging parameters through automated algorithms, the system achieves rapid accurate diagnosis without time-consuming biopsy procedures.
3Measurement precision
If extensive imaging observation by experienced surgeons is used, then diagnostic accuracy is improved, but subjectivity and time consumption increase
Solution Approach 1:
The patent enables the imaging system to serve itself through automated deep learning algorithms. The convolutional neural networks automatically extract features, identify patterns, and classify tumors without requiring human interpretation, thereby eliminating subjectivity while maintaining high diagnostic accuracy.
Solution Approach 2:
The patent replaces the human expert observation system with an automated computer vision system. The deep learning models substitute for surgeon expertise, providing objective, consistent, and reproducible diagnostic results free from human subjectivity and variability.
Data Source
AI summary
A brain tumor types distinguish system includes an image outputting device and a server computing device. The image outputting device outputs at least three brain images captured from the position of a brain tumor. The server computing device pre-stores a plurality of distinguish pathways corresponding to different types of brain tumors. The server computing device includes an image receiving module, an image pre-processing module, a data comparison module and a distinguish module. The image receiving module receives the brain images. The image pre-processing module pre-processes the brain images to obtain corresponding processed images thereof. The data comparison module compares the brain images and the processed images with the distinguish pathways to obtain at least three comparison results. The distinguish module statistically analyzes the comparison results to obtain a distinguish result.