Automatic Detection of Lumbar Spine Disc Herniation

Advanced deep-learning approaches have set new standards for computer vision and pattern recognition. However, the complexity of medical images frequently impedes the creation of high-quality ground truth data. In this article, we offer a method for autonomously generating ground truth data from MRI images using instance segmentation, with a novel confidence and consistency metric to assess data quality. We employ an artificial intelligence-based system to annotate regions of interest in MRI images, leveraging Mask R-CNN—a deep neural network architecture with a mean average precision of 98% for localising and identifying discs. Subsequently, the region of interest is classified with an accuracy of 70%. Our approach facilitates radiologists by automating the detection of regions of interest in MRI images, leading to more efficient and reliable diagnoses with assured quality data. This research made significant advances by developing an automated system for medical image segmentation and implementing cutting-edge neural network technologies.

@article{Masarweh2024,
  title     = {Automatic Detection of Lumbar Spine Disc Herniation},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {11},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Mohammed Al Masarweh and Olukola Oluseyi and Ala Alkafri and Hiba Alsmadi and Tariq Alwadan},
  doi       = {10.14569/IJACSA.2024.0151112},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151112}
}