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Deep Learning for Lumbar Disc Herniation Diagnosis by Renio Reni is a document available to read on EtoBox.
This study presents a deep learning model using U-net architecture for diagnosing lumbar disc herniation from MRI images, involving a dataset of 590 patients. The model demonstrated high accuracy in detecting and segmenting herniation areas, with F1 values of 0.971 and 0.903 in sagittal and transverse planes, respectively. The findings suggest that deep learning can significantly enhance the diagnostic process for lumbar degenerative diseases, improving clinical outcomes.
- Author
- Renio Reni
- Language
- EN