Classification of lumbar spondylosis from MRI images using CNN ensemble method

Abstract

Abstract Background: Due to an unfavorable ratio between the mechanical load and the size of the intervertebral discs, lumbar spondylosis, one of the most common causes of morbidity and disability. The preferred imaging technique for determining the origins of complex lower back pain is MRI. Healthcare systems in underdeveloped countries have a shortage of radiologists. Developing a CNN ensemble model for diagnosing lumbar spondylosis from MRI images was the aim of this study. Methods: 11158 T1 and T2 labeled MRI scans were collected from the University of Gondar specialized hospital and prepared for image processing. Since the median filter performed better than the others, it was chosen to denoise the data. The data was then augmented and split into an 80:20 train test ratio. An ensemble model was built by concatenating the proposed CNN and VGG19 models. Finally, the model was deployed. Results: An ensemble model achieved strong performance of 98.16% accuracy, 98% recall, and 98% precision. The GUI provides a setting appropriate for routine model usage. Conclusion: The research confirms that lumbar spondylosis can be diagnosed using MRI data and a CNN ensemble model.