An Optimized Explainable Cross‐Attention Transformer With Separable Convolutions for Multimodal Chronic Kidney Disease Detection

ABSTRACT One of the life‐threatening health conditions that affects millions of people across the globe is chronic kidney disease. Early detection and classification play an important role in treating and controlling the disease progression. The traditional healthcare system faces various challenges, like the risks such as progression of end‐stage renal disease, high morbidity and mortality due to the increasing number of chronic kidney disease patients across the world. Millions of lives can be saved with early detection and proper treatment. This study proposes a novel optimized explainable cross attention transformer‐based separable neural network model to detect and classify chronic kidney disease. In this model, the preprocessing approaches like image resizing, data augmentation, image normalization, missing data handling, data encoding, and data imputation are used to clean the data. Then, to choose the optimal attributes from the preprocessed data, a separable convolutional network and a transformer encoder are utilized. The softmax activation function and the fully connected layers in the classification layers perform the multiclass classification of this data. The interpretability and transparency of the model are improved using local interpretable model‐agnostic explanations, and the convergence rate and training are enhanced with the integration of stochastic gradient descent optimizer. Two publicly accessible kindney disease‐related datasets are used in this work to validate the model performance. The experiments conducted indicated that the proposed model attained the superior accuracy value of 98.45% and the lowest error rate of 1.55% and showed its superiority over the existing techniques.