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

Abstract

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.