Abstract
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.