Accurate classification of kidney abnormalities from computed tomography (CT) images is essential for early diagnosis and clinical decision-making. However, distinguishing kidney stones, cysts, tumours, and normal kidneys remains challenging because of variations in lesion size, appearance, and imaging conditions. This study proposes a multi-resolution attention-fused, calibration-aware deep learning framework for four-class kidney CT image classification. The framework integrates convolutional neural networks and a Vision Transformer to extract complementary local and global features, which are combined through an adaptive attention-based fusion mechanism. Calibration-aware learning, incorporating focal loss, multi-class Brier loss, and feature-level orthogonality regularization, is employed to improve prediction reliability and confidence estimation. The model was trained and evaluated using an image-level data split on a publicly available kidney CT dataset containing normal, cyst, stone, and tumour images. Experimental results demonstrate an overall classification accuracy of 99.3%, a Macro-F1 score of 99.1%, an AUROC of 0.999, and an AUPRC of 0.998 on the independent test set. Robustness analysis under synthetic image corruptions showed only a modest reduction in performance, with the AUROC remaining at 0.995 under severe motion blur conditions. External validation on two independent cohorts (External Site-A and External Site-B) achieved AUROC values of 0.993 and 0.988, respectively, while post-hoc temperature scaling improved calibration by reducing the Expected Calibration Error (ECE) from 0.028 to 0.012 and 0.034 to 0.015. These results demonstrate that the proposed framework provides accurate, robust, and well-calibrated predictions, supporting its potential application in computer-aided kidney CT image analysis.