MSKd_Net: Multi-Head Attention-Based Swin Transformer for Kidney Diseases Classification

In renal disease classification, various pathologies such as kidney stones, renal cysts, and tumors pose significant health risks and diagnostic challenges. Annually, thousands of cases are diagnosed worldwide, impacting both pediatric and adult populations. To address this, Computer Assisted Diagnosis (CAD) systems have emerged, aiming to alleviate the burden on healthcare professionals, enhance diagnostic accuracy, and manage the vast amounts of medical data involved. These CAD systems traditionally leverage the expertise of nephrologists, specialized imaging features, and domain knowledge to aid in disease classification. The MSKd_Net architecture integrates Swin Transformer-based hierarchical learning, efficiently capturing both local and global features, along with a customized convolved model enhanced with a multi-head attention layer. Additionally, the custom convolved model enhanced with the multi-head attention layer efficiently captures long-range dependencies and relationships in the input sequence or set of features, resulting in fine-tuned features. The Swin Transformer features are fused additively with the convolved model enhanced with multi-head attention layer features and fed to linear layers to classify the input such as cyst, stone, tumor, and normal. The proposed method significantly enhanced the discrimination capability of the MSKd_Net model on the Riñones dataset, achieving optimal F1-scores of 0.97, 0.99, 0.98, and 0.9 for cyst, normal, stone, and tumor, respectively on testing data. The strategic fusion of these components effectively addresses classification challenges, enhancing crucial feature identification for accurate multi-class kidney disease image recognition, and seamlessly integrates within MSKd_Net to achieve superior performance.