ArticleComputational intelligence and neuroscience2022
MRI-Based Medical Image Recognition: Identification and Diagnosis of LDH.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Development and validation of the reported outcome scale for patients with lumbar disc herniation.Annals of medicine · 2025Article
- MRI grading of lumbar disc herniation based on AFFM-YOLOv8 system.Scientific reports · 2025Article
- Special Issue: Artificial Intelligence in Advanced Medical Imaging.Bioengineering (Basel, Switzerland) · 2024Article
- Application of image recognition technology in pathological diagnosis of blood smears.Clinical and experimental medicine · 2024Review
- A New Medical Analytical Framework for Automated Detection of MRI Brain Tumor Using Evolutionary Quantum Inspired Level Set Technique.Bioengineering (Basel, Switzerland) · 2023Article
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Authors and funding
5 authors.
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Abstract
To realize the automatic symptom recognition and classification of MR images and improve the accuracy and efficiency of the diagnosis of lumbar intervertebral disc herniation (LDH), a method for lumbar intervertebral disc recognition and disease classification is proposed in this paper. The method mainly includes three steps: preprocessing, target segmentation, and symptom classification. Preprocessing is performed by noise reduction and interference removal methods for blurred images. The contour poles are used to determine the four points of the tail vertebra in order to reduce the wrong segmentation of the tail vertebra. A classification method based on five judgment indicators is proposed, which effectively improves the stability of disease diagnosis. The example verifies that the algorithm can accurately complete the target segmentation and the accuracy of symptom classification reaches the standard of professional doctors, which proves that the method has good robustness.
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