Opportunities and challenges of machine learning models for prediction and diagnosis of spondylolisthesis: a systematic review

Saravagi, Deepika; Agrawal, Shweta; Saravagi, Manisha

doi:10.1504/ijesms.2021.115534

Cited by 9 publications

(3 citation statements)

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“…In computer vision and radiography, convolutional neural networks (CNN) are a class of artificial neural networks that are gaining a lot of popularity. CNN is used to automatically detect features from the images using a number of layers like convolution layers, pooling layers, and fully connected layers ( 7 , 8 ). CNN uses successive convolution and pooling layers to classify the images.…”

Section: Introductionmentioning

confidence: 99%

VCNet: Hybrid Deep Learning Model for Detection and Classification of Lung Carcinoma Using Chest Radiographs

Tandon¹,

Agrawal²,

Chang

et al. 2022

Front. Public Health

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Detection of malignant lung nodules from Computed Tomography (CT) images is a significant task for radiologists. But, it is time-consuming in nature. Despite numerous breakthroughs in studies on the application of deep learning models for the identification of lung cancer, researchers and doctors still face challenges when trying to deploy the model in clinical settings to achieve improved accuracy and sensitivity on huge datasets. In most situations, deep convolutional neural networks are used for detecting the region of the main nodule of the lung exclusive of considering the neighboring tissues of the nodule. Although the accuracy achieved through CNN is good enough but this models performance degrades when there are variations in image characteristics like: rotation, tiling, and other abnormal image orientations. CNN does not store relative spatial relationships among features in scanned images. As CT scans have high spatial resolution and are sensitive to misalignments during the scanning process, there is a requirement of a technique which helps in considering spatial information of image features also. In this paper, a hybrid model named VCNet is proposed by combining the features of VGG-16 and capsule network (CapsNet). VGG-16 model is used for object recognition and classification. CapsNet is used to address the shortcomings of convolutional neural networks for image rotation, tiling, and other abnormal image orientations. The performance of VCNeT is verified on the Lung Image Database Consortium (LIDC) image collection dataset. It achieves higher testing accuracy of 99.49% which is significantly better than MobileNet, Xception, and VGG-16 that has achieved an accuracy of 98, 97.97, and 96.95%, respectively. Therefore, the proposed hybrid VCNet framework can be used for the clinical purpose for nodule detection in lung carcinoma detection.

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Section: Introductionmentioning

confidence: 99%

VCNet: Hybrid Deep Learning Model for Detection and Classification of Lung Carcinoma Using Chest Radiographs

Tandon¹,

Agrawal²,

Chang

et al. 2022

Front. Public Health

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“…Approaches for categorizing and detecting vertebral column diseases typically include image processing techniques. Image classification has long been a research hotspot, and Deep Learning (DL) methods provide a wide range of capabilities and flexibility that can be used in image classification [ 7 ]. Convolutional Neural Network (CNN) is the most popular type of Deep Neural Network (DNN) that uses multilayer pixel-based Artificial Neural Network (ANN) methods [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Lumbar Spondylolisthesis Using Optimized Pretrained CNN Models

Saravagi¹,

Agrawal²,

Saravagi

et al. 2022

Computational Intelligence and Neuroscience

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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.

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“…Machine learning-based technologies have been successfully applied in a variety of domains for prediction and identification. Researchers have used machine learning for plant disease detection [2], for pandemic handling [3], cancer diagnostics [4], spondylolisthesis prediction [5], and stock predictions [6]. ere exist many challenges in processing machine learning models [7].…”

Section: Introductionmentioning

confidence: 99%

Novel PSO Optimized Voting Classifier Approach for Predicting Water Quality

Agrawal¹,

Jain

Khatri³

et al. 2022

Mathematical Problems in Engineering

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Over the last few years, different contaminants have posed a danger to the quality of the water. Hence modelling and forecasting water quality are very important in the management of water contamination. The paper proposes an ensemble machine learning-based model for assessing water quality. The results of the proposed model are compared with several machine learning models, including k-nearest neighbour, Naïve Bayes, support vector machine, and decision tree. The considered dataset contains seven statistically important parameters: pH, conductivity, dissolved oxygen, Biochemical Oxygen Demand, nitrate, total coliform, and fecal coliform. The water quality index is calculated for assessing water quality. To utilize an ensemble approach, a voting classifier has been designed with hard voting. The highest prediction accuracy of 99.5% of the water quality index is presented by the voting classifier as compared to the prediction accuracy of 99.2%, 90%, 79%, and 99% presented through k-nearest neighbour, Naïve Bayes, support vector machine, and decision tree, respectively. This was further enhanced to 99.74% using particle swarm based optimization.

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Opportunities and challenges of machine learning models for prediction and diagnosis of spondylolisthesis: a systematic review

Cited by 9 publications

References 0 publications

VCNet: Hybrid Deep Learning Model for Detection and Classification of Lung Carcinoma Using Chest Radiographs

VCNet: Hybrid Deep Learning Model for Detection and Classification of Lung Carcinoma Using Chest Radiographs

Diagnosis of Lumbar Spondylolisthesis Using Optimized Pretrained CNN Models

Novel PSO Optimized Voting Classifier Approach for Predicting Water Quality

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