HeCapsNet: An enhanced capsule network for automated heel disease diagnosis using lateral foot X-Ray images

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dc.authoridOZACAR, KASIM/0000-0001-7637-0620
dc.contributor.authorTaher, Osamah
dc.contributor.authorOzacar, Kasim
dc.date.accessioned2024-09-29T15:50:42Z
dc.date.available2024-09-29T15:50:42Z
dc.date.issued2024
dc.departmentKarabük Üniversitesien_US
dc.description.abstractFoot pain, particularly caused by heel spurs and Sever's disease, significantly impacts mobility and daily activities for many people. These diseases are traditionally diagnosed by orthopedic specialists using X-ray images of the lateral foot. In certain situations, the absence of specialists requires the adoption of AI-based methods; however, the lack of a dataset hinders the use of AI for the preliminary diagnosis of these diseases. Therefore, this study first presents a novel dataset consisting of 3956 annotated lateral foot X-ray images and uses the original capsule network (CapsNet) to automatically detect and classify heel bone diseases. The low accuracy of 73.99% of CapsNet due to the low extraction feature layers led us to search for a new model. For this reason, this paper also proposes a new enhanced capsule network (HeCapsNet) by adjusting the features extraction layers, adding extra convolutional layers, using he normal kernel initializer instead of normal and utilizing the same padding scheme to perform better with medical images. Evaluating the performance of the proposed model, higher accuracy rates are achieved, including 97.29% for balanced data, 94.19% for imbalanced data, area under the curve (AUC) of 98.69%, and a fivefold cross-validation accuracy of 95.77%. We then compared our proposed model with state-of-the-art modified CapsNet models using various datasets (MNIST, Fashion-MNIST, CIFAR10, and brain tumor). HeCapsNet performed similarly to modified CapsNets on relatively simple non-medical datasets such as MNIST and Fashion-MNIST, but performed better on more complex medical datasets.en_US
dc.description.sponsorshipTUBITAK, The Scientific and Technological Research Council of Turkiyeen_US
dc.description.sponsorshipWe would like to express our sincere thanks to Dr. UEmit OEzguer GUELER, Specialist in Orthopedics and Traumatology, for his efforts and time in marking the foot x-ray images and his dedication in facilitating the conduct of this study. This work was supported by TUBITAK, The Scientific and Technological Research Council of Turkiye.en_US
dc.identifier.doi10.1002/ima.23084
dc.identifier.issn0899-9457
dc.identifier.issn1098-1098
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85191813795en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1002/ima.23084
dc.identifier.urihttps://hdl.handle.net/20.500.14619/3694
dc.identifier.volume34en_US
dc.identifier.wosWOS:001214543300001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherWileyen_US
dc.relation.ispartofInternational Journal of Imaging Systems and Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectcapsule networken_US
dc.subjectdeep learningen_US
dc.subjectfoot X-ray imagesen_US
dc.subjectheel spuren_US
dc.subjectseveren_US
dc.titleHeCapsNet: An enhanced capsule network for automated heel disease diagnosis using lateral foot X-Ray imagesen_US
dc.typeArticleen_US

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