Kidney abnormalities, when detected late, can develop into chronic conditions such as kidney tumors and cardiorenal syndrome. Recently, there has been a move toward automation, especially as it pertains to developing a framework for advanced preprocessing, feature fusion, and hybrid spatial-temporal learning for the detection of the abnormalities in question. For example, Non-Local Means (NLM) filtering for noise reduction, image quality improvements through U-Net kidney segmentation and deep learning-based text annotation removal, as well as robust training through data augmentation and preprocessing ends up facilitating the process. The combination of classical texture and shape descriptors and deep embeddings from a Vision Transformer (ViT) through an attention mechanism allows the model to pinpoint where clinically relevant focus should be. A hybrid CNN-LSTM framework attends to spatial and temporal feature extraction, while attention modules receive the classification output to refine it. The incorporation of weighted binary cross-entropy for handling class imbalance, and of explainable AI, is demonstrated through Integrated Gradients. Experimental results for VATLA show it used advanced algorithms and automation principles in developing systems for all previous performance indicators, while providing evidence of scoring 0.94 in accuracy, 0.92 in precision, 0.95 in recall, and 0.96 in AUC, or area under the curve.