Kidney failure represents a pressing global health concern, further exacerbated by the widespread shortage of nephrologists, thereby necessitating the development of ِِِArtificial Intelligence (AI)-driven systems for automated renal disease diagnosis. This study focuses on the diagnosis of three major renal conditions: kidney stones, tumors, and cysts. Recent advancements in Deep Learning (DL) have highlighted the potential of attention mechanisms in enhancing the performance of Convolutional Neural Networks (CNNs), particularly in medical image analysis. In this context, we propose a novel method termed Pyramid Channel and Spatial Attention (PCSA), which depends on pyramidal multiscale convolution to reconstruct feature representations by extracting both spatial and channel attention weights. This dual-weight extraction facilitates the precise integration of multiscale contextual information, thereby improving the model capability to localize and focus on complex regions within medical images. The PCSA module is designed as a plug-and-play component that can be seamlessly integrated into various CNN backbone architectures to enhance diagnostic accuracy. To validate its effectiveness, we incorporate the PCSA module into several backbone networks and evaluate its performance. Experimental results demonstrate that PCSA-enhanced networks outperform multiple state-of-the-art image classification methods, achieving superior accuracy in renal disease classification. Although the current study focuses on three specific renal conditions, the modular architecture of PCSA-Net allows for future adaptation to a broader spectrum of renal pathologies. These findings underscore the potential of the proposed PCSA module to support automated, accurate, and scalable kidney disease diagnosis in clinical settings. The modular design also enhances the model suitability for real-world deployment, enabling integration into diverse diagnostic workflows.
Tawfik et al. (Mon,) studied this question.