Natalia, Friska and Sudirman, Sud (2023) Classification of Sagittal Lumbar Spine MRI for Lumbar Spinal Stenosis Detection using Transfer Learning of a Deep Convolutional Neural Network. IEEE Explore.
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Classification of Sagittal Lumbar Spine MRI for Lumbar Spinal Stenosis Detection using Transfer Learning of a Deep Convolutional Neural Network.pdf Download (2MB) | Preview |
Abstract
Analysis of sagittal lumbar spine MRI images remains an important step in automated detection and diagnosis of lumbar spinal stenosis. There are numerous algorithms proposed in the literature that can measure the condition of lumbar intervertebral discs through analysis of the lumbar spine in the sagittal view. However, these algorithms rely on using suitable sagittal images as their inputs. Since an MRI data repository contains more than just these specific im- ages, it is, therefore, necessary to employ an algorithm that can automatically select such images from the entire repository. In this paper, we demonstrate the application of an image classification method using deep convolutional neural networks for this purpose. Specifically, we use a pre-trained Inception-ResNet- v2 model and retrain it using two sets of T1-weighted and T2-weighted images. Through our experiment, we can conclude that this method can reach a perfor- mance level of 0.91 and 0.93 on the T1 and T2 datasets, respectively when meas- ured using the accuracy, precision, recall, and f1-score metrics. We also show that the difference in performance between using the two modalities is statisti- cally significant and using T2-weighted images is preferred over using T1- weighted images.
| Item Type: | Article |
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| Subjects: | 000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 005 Computer Programming > 005.2 Programming for Specific Computers, Algorithm, HTML, PHP, java, C++ |
| Divisions: | Faculty of Engineering & Informatics > Information System |
| Depositing User: | Administrator UMN Library |
| Date Deposited: | 06 Nov 2023 08:09 |
| Last Modified: | 12 Jun 2024 06:28 |
| URI: | https://kc.umn.ac.id/id/eprint/27043 |
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