Yazar "Taher, Osamah" seçeneğine göre listele

Listeleniyor 1 - 2 / 2
  • Küçük Resim Yok
    Öğe
    HeCapsNet: An enhanced capsule network for automated heel disease diagnosis using lateral foot X-Ray images
    (Wiley, 2024) Taher, Osamah; Ozacar, Kasim
    Foot 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.
  • Küçük Resim Yok
    Öğe
    MedCapsNet: A modified Densenet201 model integrated with capsule network for heel disease detection and classification
    (Cell Press, 2024) Taher, Osamah; Ozacar, Kasim
    Conditions affecting the heel bone, such as heel spurs and sever's disease, pose significant challenges to patients' daily activities. While orthopedic and traumatology doctors rely on foot X-rays for diagnosis, there is a need for more AI-based detection and classification of these conditions. Therefore, this study addresses this need by proposing MedcapsNet, a novel hybrid capsule model combining modified DenseNet201 with a capsule network, designed to accurately detect and classify heel bone diseases utilizing lateral heel x-ray foot images. We conducted a comprehensive series of experiments on the proposed hybrid architecture with several datasets, including the Heel dataset, Breast BreaKHis v1, HAM10000 skin cancer dataset, and Jun Cheng Brain MRI dataset. The first experiment evaluates the proposed model for heel diseases, while the other experiments evaluate the model on a range of medical datasets to demonstrate its performance over existing studies. On the heel dataset, MedCapsNet achieves an accuracy of 96.38%, AUC of 98.35% without data augmentation, cross-validation accuracy of 95.69%, and AUC of 98.87%. The proposed model, despite employing a fixed architecture and hyperparameters, outperformed other models across four distinct datasets, including MRI, X-ray, and microscopic images with various diseases. This is notable because different types of medical image datasets typically require different architectures and hyperparameters to achieve optimal performance.