Diagnosis of Lumbar Spondylolisthesis Using Optimized Pretrained CNN Models

Abstract

Spondylolisthesis refers to the slippage of one vertebral body over the adjacent one. It is a chronic condition that requires early detection to prevent unpleasant surgery. The paper presents an optimized deep learning model for detecting spondylolisthesis in X-ray radiographs. The dataset contains a total of 299 X-ray radiographs from which 156 images are showing the spine with spondylolisthesis and 143 images are of the normal spine. Image augmentation technique is used to increase the data samples. In this study, VGG16 and InceptionV3 models were used for the image classification task. The developed model is optimized by utilizing the TFLite model optimization technique. The experimental result shows that the VGG16 model has achieved a 98% accuracy rate, which is higher than InceptionV3’s 96% accuracy rate. The size of the implemented model is reduced up to four times so it can be used on small devices. The compressed VGG16 and InceptionV3 models have achieved 100% and 96% accuracy rate, respectively. Our finding shows that the implemented models were outperformed in the diagnosis of lumbar spondylolisthesis as compared to the model suggested by Varcin et al. (which had a maximum of 93% accuracy rate). Also, the developed quantized model has achieved higher accuracy rate than Zebin and Rezvy’s (VGG16 + TFLite) model with 90% accuracy. Furthermore, by evaluating the model’s performance on other publicly available datasets, we have generalised our approach on the public platform.

An Approach to Classifying X-Ray Images of Scoliosis and Spondylolisthesis Based on Fine-Tuned Xception Model

“…Moreover, Deepika Saravagi et al collected 229 Xray images which include spondylolisthesis and the normal spine (i.e., 156 spondylolisthesis and 143 normal) which were optimized by applying the TFLite model optimization technique. As a result, the model reaches a high accuracy rate including the VGG16 model of 98% and InceptionV3 of 96% [15]. Additionally, Fatih Varçın et al also AlexNet and GoogleLeNet models to classify the data set consisting of 272 X-ray images.…”

“…For instance, Mohammad Fraiwan et al used transfer learning in the DensNet-201 model and reached a mean accuracy and maximum accuracy for spine illness classification were 96.73% and 98.02%, respectively [17]. Furthermore, Using the VGG16 model for feature extraction and CapsNet for disease identification, Deepika Saravagi's experimental results show 98% accuracy [18]. The dataset contains 466 X-ray radiographs, with 186 images showing a spine with spondylolisthesis and 280 images showing a normal spine.…”

Lumbar Spine Disease Detection: Enhanced CNN Model With Improved Classification Accuracy

“…The authors [30] proposed a recurrent neural network (RNN) [27] for lumbar spine disorder detection. An RNN-based network will allow the layers to establish connections with cycles or similar features that otherwise are not allowed within a CNN [53].…”

Classification of lumbar spondylosis from MRI images using CNN ensemble method

“…An improved model was developed using the TFLite model optimization technique. The model skips crucial image processing methods like noise removal that could improve the model's performance and only uses SoftMax as a classi er [9]. Two separate deep neural networks, AlexNet and GoogLeNet, were utilized to diagnose two classes of spondylolisthesis.…”