Enhancing Accuracy in Kidney Disease Prediction Using a CNN-Transformer Hybrid Model on Ultrasound Images

Abstract

Kidney Disease (KD) is characterized by a gradual decline in kidney function, which can eventually lead to kidney damage or failure. As the disease progresses, diagnosis becomes more challenging. Incorporating routine clinical data to assess different stages of KD can aid in early detection and timely intervention. Advanced stages of KD are associated with a higher risk of cardiovascular complications and mortality. Ultrasound (US) imaging is widely used in clinical practice for predicting KD due to its safety, convenience, and affordability. However, manual analysis of US images is time-consuming, prone to errors, and requires highly skilled professionals. In recent years, Deep Learning (DL) has shown promising results in medical image analysis. This research introduces a hybrid DL network, Convolutional Neural Network (CNN)-Transformer, designed to predict KD from US images. To conduct the study, US images of both healthy and diseased kidneys were collected from Aadhar Diagnostic Centre, Maharashtra. The collected raw images underwent several pre-processing steps, including resizing and augmentation. The processed dataset was then split into training, validation, and test sets in a 7:2:1 ratio. The proposed hybrid network was compared with well-known DL networks, ResNet and DenseNet. All three models were trained, validated, and tested under identical conditions, including the same number of images, epochs, and hyperparameters, to ensure a fair comparison. The models were tested on 25 healthy and 25 diseased images. The results showed that DenseNet and ResNet correctly predicted 44 and 43 cases, respectively, while the proposed CNN-Transformer network achieved 49 correct predictions out of 50 samples. The proposed network attained the highest accuracy of 98%, whereas DenseNet and ResNet achieved 88% and 86%, respectively. In addition to accuracy, other evaluation metrics, including Precision, Recall, and F1-Score, were also significantly higher for the proposed network compared to the other two networks. These findings demonstrate that the proposed CNN-Transformer network delivers promising results for KD prediction using US images.