Automatic localization AI modeling of herniation in lumbar spine vertebrae

Abstract

Lumbar intervertebral disc herniation is a prevalent medical condition newlinenecessitating a precise diagnosis for effective therapeutic interventions. newlineParticularly Magnetic Resonance Imaging (MRI) is essential for identifying the newlineherniated spinal discs. The manual diagnostic method is both time-consuming newlineand subjective by emphasizing the importance of automated solutions. The newlineproposed work addresses the intricate challenges with lumbar intervertebral newlinedisc herniation detection using an advanced automatic approach. newlineThe initial segment of the thesis focuses on the pivotal role of MRI in newlinelumbar intervertebral disc herniation detection. MRI provides a detailed newlineinsight into the complex structures of the spine by contributing significantly newlineto the accuracy of diagnosis. Manual methods are prone to subjectivity, which newlineunderscores the pressing need for more efficient and objective diagnostic newlineapproaches. The proposed work introduces a multifaceted automated newlineapproach leveraging advanced image processing and deep learning newlinetechniques, to overcome the limitations of manual diagnosis. This automated newlineapproach aims to enhance the accuracy and efficiency of lumbar intervertebral newlinedisc herniation detection by extracting quantitative features from spine MRI newlineimages. This approach has the potential to revolutionize the diagnostic newlineprocess and improve patient outcomes, by reducing subjectivity and human newlineerror. newline