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Predicting Lumbar Spondylolisthesis: A Hybrid Deep Learning Approach

Intelligent Automation & Soft Computing 2023年第8期37卷 2133-2151页

作者： Deepika Saravagi Shweta Agrawal Manisha Saravagi Sanjiv K.Jain bhisham sharma Abolfazl Mehbodniya Subrata Chowdhury Julian L.Webber Department of Computer Application SAGE UniversityIndore452012Madhya PradeshIndia IAC SAGE UniversityIndore452012Madhya PradeshIndia Shivang College of Physiotherapy Ratlam4570021Madhya PradeshIndia Medi-Caps University Rau IndoreMadhya Pradesh453331India Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjabIndia Department of Electronics and Communication Engineering Kuwait College of Science and Technology(KCST)DohaKuwait Department of Computer Science and Engineering Sreenivasa Institute of Technology and Management StudiesChittoorAndra PradeshIndia

Spondylolisthesis is a chronic disease,and a timely diagnosis of it may help in avoiding *** identification in x-ray radiographs is very *** the feature extraction tool in VGG16 has improved the classification *** the fully connected layers of VGG16 are not efficient at capturing the positional structure of an object in *** network(CapsNet)works with capsules(neuron clusters)rather than a single neuron to grasp the properties of the provided image to match the *** this study,an integrated model that is a combination of VGG16 and CapsNet(S-VCNet)is *** the model,VGG16 is used as a feature *** feature extraction,the output is fed to CapsNet for disease identification.A private dataset is used that contains 466 X-ray radiographs,including 186 images displaying a spine with spondylolisthesis and 280 images depicting a normal *** suggested model is the first step towards developing a web-based radiological diagnosis tool that can be utilized in outpatient clinics where there are not enough qualified medical *** results demonstrate that the developed model outperformed the other models that are used for lumbar spondylolisthesis diagnosis with 98%*** the performance check,the model has been successfully deployed on the Gradio web app platform to produce the outcome in less than 20 s.

关键词： Gradio lumbar spondylolisthesis transfer learning VGG16 machine learning deep learning

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