Previously submitted to: Journal of Medical Internet Research (no longer under consideration since Oct 18, 2023)
Date Submitted: May 8, 2023
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Brain tumor MRI intelligent diagnosis based on U-Net feature extraction
ABSTRACT
Background:
Classifying brain tumors preoperatively provides essential information for guiding treatment plans. However, existing classification methods require manual intervention which often suffer from efficiency and accuracy issues.
Objective:
The objective of our study was to Improve the accuracy and efficiency of brain tumor grading or classification. This shows that our method is to help clinicians diagnose brain tumor and formulate treatment plans.
Methods:
Our proposed method uses a DenseNet-ResNet based U-Net framework to optimize the task of extracting features from brain tumor MRI image data. It also adopts a CRNN model to classify brain tumors from sequence data. The characteristic of our method is that it needs only one sequence-level label, instead of many frame-level labels for each patient.
Results:
We proposed an automated MRI classification method for brain tumor, with an average accuracy of 90.72% for glioma classification, 94.35% for glioma IDH1 mutation classification, and 94.64% for pituitary tumor texture classification.
Conclusions:
Compared with existing purely auto-encoder based methods, ours has much better efficiency.
Citation
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Copyright
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