This thesis examines the potential of Deep Learning (DL) technologies in improving the diagnosis of Lumbar Spinal Stenosis (LSS) through MRI analyses. Current diagnostic methods for LSS rely heavily on the subjective interpretations of radiologists, leading to inconsistencies and potential misdiagnoses. This study aimed to develop a DL-based object detection model to minimize these issues, enhancing both the accuracy and efficiency of MRI evaluations. The research employed a pre-trained YOLO (You Only Look Once) model, adapted through transfer learning to detect signs of canal stenosis in lumbar spine MRI images. The open-source datasets of lumbar spine MRIs were preprocessed and used for training and validating the model, employing cross-validation techniques to optimize and assess its performance. The findings revealed that the adapted YOLO model achieved a mean Average Precision (mAP) of 97.1% and an Intersection Over Union (IOU) threshold of 0.5, with a classification accuracy of 93%. While these results are impressive, it is important to note that a direct comparison with existing diagnostic methods is not feasible due to differences in datasets. The dataset used in this study lacks documented labeling criteria, unlike the balanced and reliable private datasets used in prior studies. The model demonstrated high scores across various metrics, including a precision of 91.9%, recall of 93.5%, and an F1-score of 93%. This study highlights the prominent role of transfer learning in achieving high performance, suggesting that the implementation of this DL model could significantly reduce human errors and the workload on radiologists, accelerate diagnostic processes, and integrate seamlessly into clinical settings. However, limitations such as class imbalance in training data suggest the need for further research using balanced, comprehensive datasets to fully harness the capabilities of DL in clinical diagnostics. Page