Classifying Cervical Spondylosis Based on Fuzzy Calculation

Yu, Xinghu; Xiang, Liangbi

doi:10.1155/2014/182956

Cited by 9 publications

(5 citation statements)

References 25 publications

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“…Fast diagnosis is the base of the DL model for mobile and small-scale, low-cost devices [ 9 ]. But models suggested in prior studies are slow, less accurate, and unsuitable for tiny devices due to a large number of parameters.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Lumbar Spondylolisthesis Using a Pruned CNN Model

Saravagi¹,

Agrawal²,

Saravagi

et al. 2022

Computational and Mathematical Methods in Medicine

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Convolutional neural network (CNN) models have made tremendous progress in the medical domain in recent years. The application of the CNN model is restricted due to a huge number of redundant and unnecessary parameters. In this paper, the weight and unit pruning strategy are used to reduce the complexity of the CNN model so that it can be used on small devices for the diagnosis of lumbar spondylolisthesis. Experimental results reveal that by removing 90% of network load, the unit pruning strategy outperforms weight pruning while achieving 94.12% accuracy. Thus, only 30% (around 850532 out of 3955102) and 10% (around 251512 out of 3955102) of the parameters from each layer contribute to the outcome during weight and neuron pruning, respectively. The proposed pruned model had achieved higher accuracy as compared to the prior model suggested for lumbar spondylolisthesis diagnosis.

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Section: Introductionmentioning

confidence: 99%

Diagnosis of Lumbar Spondylolisthesis Using a Pruned CNN Model

Saravagi¹,

Agrawal²,

Saravagi

et al. 2022

Computational and Mathematical Methods in Medicine

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show abstract

“…Conversely, the application of artificial intelligence (AI) offers a promising avenue for the prediction and diagnosis of CS [ 22 ]. Yu [ 23 ] developed a CS classification model using fuzzy computing theory, achieving an accuracy of 80.33%, thus demonstrating the potential of machine learning in classifying and processing various imaging features effectively.…”

Section: Discussionmentioning

confidence: 99%

Cervical Spondylosis Diagnosis Based on Convolutional Neural Network with X-ray Images

Xie,

Nie,

Lundgren

et al. 2024

Sensors

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The increase in Cervical Spondylosis cases and the expansion of the affected demographic to younger patients have escalated the demand for X-ray screening. Challenges include variability in imaging technology, differences in equipment specifications, and the diverse experience levels of clinicians, which collectively hinder diagnostic accuracy. In response, a deep learning approach utilizing a ResNet-34 convolutional neural network has been developed. This model, trained on a comprehensive dataset of 1235 cervical spine X-ray images representing a wide range of projection angles, aims to mitigate these issues by providing a robust tool for diagnosis. Validation of the model was performed on an independent set of 136 X-ray images, also varied in projection angles, to ensure its efficacy across diverse clinical scenarios. The model achieved a classification accuracy of 89.7%, significantly outperforming the traditional manual diagnostic approach, which has an accuracy of 68.3%. This advancement demonstrates the viability of deep learning models to not only complement but enhance the diagnostic capabilities of clinicians in identifying Cervical Spondylosis, offering a promising avenue for improving diagnostic accuracy and efficiency in clinical settings.

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“…The fuzzy model was constructed based on Gaussian MF type, whereby they were generated using statistical and probability calculations, as well as opinions from experts. Yu and Xiang (2014) [71] proposed an approach to reduce the issues of subjectivity, time consuming, and errors in X-ray radiographs evaluation, which is based on human experiences (clinician). Their work investigated the classification of cervical spondylosis (CS) using fuzzy calculation.…”

Section: Shi Et Al (2014)mentioning

confidence: 99%

Generating Clustering-Based Interval Fuzzy Type-2 Triangular and Trapezoidal Membership Functions: A Structured Literature Review

et al. 2021

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Clustering is more popular than the expert knowledge approach in Interval Fuzzy Type-2 membership function construction because it can construct membership function automatically with less time consumption. Most research proposed a two-fuzzifier fuzzy C-Means clustering method to construct Interval Fuzzy Type-2 membership function which mainly focused on producing Gaussian membership function. The other two important membership functions, triangular and trapezoidal, are constructed using the grid partitioning method. However, the method suffers a drawback of not being able to represent actual data composition in the underlying dataset. Some research proposed triangular and trapezoidal membership functions construction using readily formed Fuzzy Type-1 membership functions, which means it remains unclear how the membership functions are heuristically constructed using fuzzy C-Means outputs. The triangular and trapezoidal membership functions are important because previous works have shown that they may produce superior performance than Gaussian membership function in some applications. Therefore, this paper presents a structured literature review on generating triangular and trapezoidal Interval Fuzzy Type-2 membership functions using fuzzy C-Means. Initially, 110 related manuscripts were collected from Web of Science, Scopus, and Google Scholar. These manuscripts went through the identification, screening, eligibility, and inclusion processes, and as a result, 21 manuscripts were reviewed and discussed in this paper. To ensure that the review also covers the important components of fuzzy logic, this paper also reviews and discusses another 49 manuscripts on fuzzy calculation and operation. Furthermore, this paper also discusses the contributions of the conducted review to the body of knowledge, future research directions and challenges, with the aim to motivate the future works of constructing the methods to generate Interval Fuzzy Type-2 triangular and trapezoidal membership functions using fuzzy C-Means. The methods imply flexibility in choosing membership function type, hence increasing the effectiveness of fuzzy applications through leveraging the advantages that each of the three membership function types could provide.

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Classifying Cervical Spondylosis Based on Fuzzy Calculation

Cited by 9 publications

References 25 publications

Diagnosis of Lumbar Spondylolisthesis Using a Pruned CNN Model

Diagnosis of Lumbar Spondylolisthesis Using a Pruned CNN Model

Cervical Spondylosis Diagnosis Based on Convolutional Neural Network with X-ray Images

Generating Clustering-Based Interval Fuzzy Type-2 Triangular and Trapezoidal Membership Functions: A Structured Literature Review

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