Detection of Flatfoot Deformity from X-Ray Images Using Image Filtering and Transfer Learning Approaches

Flatfoot (pes planus) is a condition defined as the flattening of the curved structure as a result of the collapse of the foot or the weakening of the structures, such as ligaments and muscles that hold the bones and tissues in the foot in a certain order and a curve due to various reasons. If left untreated, this condition can lead to calf, knee, hip, and lower back pain and even postural disorders due to foot deterioration. In this study, a transfer learning-based method is presented using the Dilation filter for flatfoot detection from X-ray images. The X-ray image dataset contains 402 flatfoot images and 440 control images. For image preprocessing, dilation filtering is used, and the images are enhanced with the dilation method. After image preprocessing, the performance of transfer learning approaches, DarkNet19, GoogLeNet, DenseNet-201, ResNet-101, and MobileNetV2 architectures, were compared. The holdout method was used for performance measurements. The experimental results show that the DenseNet-201 model performs the best with an overall accuracy of 0.9802 and a Cohen's Kappa value of 0.96. The results show that the combination of dilation filtering and transfer learning methods provides an effective approach for automatic flatfoot detection. Compared to similar studies in the literature, the accuracy of the proposed model is significantly higher.