Comparison of Deep Learning Models and Optimization Algorithms in the Detection of Scoliosis and Spondylolisthesis from X-Ray Images

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dc.contributor.authorAkkaya, Abdullah Erhan
dc.contributor.authorGüneş, Harun
dc.contributor.authorHark, Cengiz
dc.date.accessioned2024-08-04T19:54:32Z
dc.date.available2024-08-04T19:54:32Z
dc.date.issued2024
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThe spine is composed of pieces of bone called vertebrae that lie between the skull and the tailbone. Various medical conditions can affect the spine. In this study, two types of degenerative diseases, scoliosis, and spondylolisthesis, were studied. Deep AI architectures have recently enabled further disease diagnosis innovation using medical images. Various traditional and deep learning studies use medical images for disease diagnosis in the literature. This study aims to classify spine X-ray images according to three possible conditions (Normal, Scoliosis, and Spondylolisthesis) and to exploit the potential of these X-ray images to detect possible diseases occurring in the spine. The performance of deep learning models and optimization algorithms used in this process was evaluated. The study uses a data set created and/or analyzed during an existing study. This data set consists of images that belong to three different classes: scoliosis, spondylolisthesis, or x-ray images of normal (i.e. healthy) individuals. A total of 338 spine X-ray images, 188 scoliosis images, 79 spondylolisthesis images, and 71 normal images. Six different deep-learning architectures have been used in the study. These architectures are Alexnet, GoogLeNet, ResNet-18, ResNet-50, ResNet-101, and EfficientNet-bo. While working on these deep architectures, each model has been evaluated using different optimization algorithms. These optimization algorithms are RmsProp, SGDM, and Adam. According to the classification processes, the deep learning model with the highest accuracy value was Alexnet, and the optimization algorithm used with it, Sgdm (99.01%), and the training time lasted 38 seconds. According to the classification processes, the deep learning model with the fastest completion time (30 seconds) was Alexnet and the optimization algorithm used with it was RmsProp. An accuracy rate of 98.02% has been obtained in the training of this model.en_US
dc.identifier.doi10.16984/saufenbilder.1246001
dc.identifier.endpage451en_US
dc.identifier.issn1301-4048
dc.identifier.issn2147-835X
dc.identifier.issue2en_US
dc.identifier.startpage438en_US
dc.identifier.trdizinid1233735en_US
dc.identifier.urihttps://doi.org/10.16984/saufenbilder.1246001
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/1233735
dc.identifier.urihttps://hdl.handle.net/11616/89878
dc.identifier.volume28en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.relation.ispartofSakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisien_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.titleComparison of Deep Learning Models and Optimization Algorithms in the Detection of Scoliosis and Spondylolisthesis from X-Ray Imagesen_US
dc.typeArticleen_US

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