Lumbar spine discs classification based on deep convolutional neural networks using axial view MRI

Mbarki, Wafa; Bouchouicha, Moez; Frizzi, Sébastien; Tshibasu, Frederick; Farhat, Leïla Ben; Sayadi, Mounir

doi:10.1016/j.inat.2020.100837

Cited by 38 publications

(23 citation statements)

References 26 publications

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“…Moreover, the second limitation is that the definition of this study detects the sagittal view regions of LDH. Although the other report uses U-net architecture to classified lumbar axial view MRI ( Mbarki et al, 2020 ). We used the object detection method with the dataset of sagittal view LDH images as a preliminary study.…”

Section: Discussionmentioning

confidence: 99%

Lumbar Disc Herniation Automatic Detection in Magnetic Resonance Imaging Based on Deep Learning

Tsai

Hung

Guo

et al. 2021

Front. Bioeng. Biotechnol.

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Background: Lumbar disc herniation (LDH) is among the most common causes of lower back pain and sciatica. The causes of LDH have not been fully elucidated but most likely involve a complex combination of mechanical and biological processes. Magnetic resonance imaging (MRI) is a tool most frequently used for LDH because it can show abnormal soft tissue areas around the spine. Deep learning models may be trained to recognize images with high speed and accuracy to diagnose LDH. Although the deep learning model requires huge numbers of image datasets to train and establish the best model, this study processed enhanced medical image features for training the small-scale deep learning dataset.Methods: We propose automatic detection to assist the initial LDH exam for lower back pain. The subjects were between 20 and 65 years old with at least 6 months of work experience. The deep learning method employed the YOLOv3 model to train and detect small object changes such as LDH on MRI. The dataset images were processed and combined with labeling and annotation from the radiologist’s diagnosis record.Results: Our method proves the possibility of using deep learning with a small-scale dataset with limited medical images. The highest mean average precision (mAP) was 92.4% at 550 images with data augmentation (550-aug), and the YOLOv3 LDH training was 100% with the best average precision at 550-aug among all datasets. This study used data augmentation to prevent under- or overfitting in an object detection model that was trained with the small-scale dataset.Conclusions: The data augmentation technique plays a crucial role in YOLOv3 training and detection results. This method displays a high possibility for rapid initial tests and auto-detection for a limited clinical dataset.

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Section: Discussionmentioning

confidence: 99%

Lumbar Disc Herniation Automatic Detection in Magnetic Resonance Imaging Based on Deep Learning

Tsai

Hung

Guo

et al. 2021

Front. Bioeng. Biotechnol.

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“…Neither transfer learning nor finetuning were performed in these tests. Apart from state-of-the-art works [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 32 , 33 , 34 , 35 ] pretrained in several medical image datasets and ImageNet, our proposal received no extra training and only took the training partition of each presented dataset. Our source code is available on .…”

Section: Resultsmentioning

confidence: 99%

“…Nowadays, a huge number of CNN models exist and are used for distinct purposes. As a result, we can find custom models [ 16 , 19 , 31 ] and the ones that use key baselines for classification of different diseases [ 17 , 22 , 24 , 29 , 41 , 42 , 43 ].…”

Section: Methodsmentioning

confidence: 99%

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NanoChest-Net: A Simple Convolutional Network for Radiological Studies Classification

et al. 2021

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The new coronavirus disease (COVID-19), pneumonia, tuberculosis, and breast cancer have one thing in common: these diseases can be diagnosed using radiological studies such as X-rays images. With radiological studies and technology, computer-aided diagnosis (CAD) results in a very useful technique to analyze and detect abnormalities using the images generated by X-ray machines. Some deep-learning techniques such as a convolutional neural network (CNN) can help physicians to obtain an effective pre-diagnosis. However, popular CNNs are enormous models and need a huge amount of data to obtain good results. In this paper, we introduce NanoChest-net, which is a small but effective CNN model that can be used to classify among different diseases using images from radiological studies. NanoChest-net proves to be effective in classifying among different diseases such as tuberculosis, pneumonia, and COVID-19. In two of the five datasets used in the experiments, NanoChest-net obtained the best results, while on the remaining datasets our model proved to be as good as baseline models from the state of the art such as the ResNet50, Xception, and DenseNet121. In addition, NanoChest-net is useful to classify radiological studies on the same level as state-of-the-art algorithms with the advantage that it does not require a large number of operations.

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“…Mbarki et al [37] studied identification of a herniated lumbar disc by working on MRI, using CNN, based on the VGG16 geometry. A special dataset was used from Sahloul University Hospital in Sousse, Tunisia.…”

Section: Related Workmentioning

confidence: 99%

A Transfer Learning Approach for Lumbar Spine Disc State Classification

Al-Kubaisi

Khamiss

2021

Electronics

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Recently, deep learning algorithms have become one of the most popular methods and forms of algorithms used in the medical imaging analysis process. Deep learning tools provide accuracy and speed in the process of diagnosing and classifying lumbar spine problems. Disk herniation and spinal stenosis are two of the most common lower back diseases. The process of diagnosing pain in the lower back can be considered costly in terms of time and available expertise. In this paper, we used multiple approaches to overcome the problem of lack of training data in disc state classification and to enhance the performance of disc state classification tasks. To achieve this goal, transfer learning from different datasets and a proposed region of interest (ROI) technique were implemented. It has been demonstrated that using transfer learning from the same domain as the target dataset may increase performance dramatically. Applying the ROI method improved the disc state classification results in VGG19 2%, ResNet50 16%, MobileNetV2 5%, and VGG16 2%. The results improved VGG16 4% and in VGG19 6%, compared with the transfer from ImageNet. Moreover, it has been stated that the closer the data to be classified is to the data that the system trained on, the better the achieved results will be.

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Lumbar spine discs classification based on deep convolutional neural networks using axial view MRI

Cited by 38 publications

References 26 publications

Lumbar Disc Herniation Automatic Detection in Magnetic Resonance Imaging Based on Deep Learning

Lumbar Disc Herniation Automatic Detection in Magnetic Resonance Imaging Based on Deep Learning

NanoChest-Net: A Simple Convolutional Network for Radiological Studies Classification

A Transfer Learning Approach for Lumbar Spine Disc State Classification

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