  Kidney stone disease results in millions of annual visits to emergency
departments in the United States. Computed tomography (CT) scans serve as the
standard imaging modality for efficient detection of kidney stones. Various
approaches utilizing convolutional neural networks (CNNs) have been proposed to
implement automatic diagnosis of kidney stones. However, there is a growing
interest in employing fast and efficient CNNs on edge devices in clinical
practice. In this paper, we propose a lightweight fusion framework for kidney
detection and kidney stone diagnosis on coronal CT images. In our design, we
aim to minimize the computational costs of training and inference while
implementing an automated approach. The experimental results indicate that our
framework can achieve competitive outcomes using only 8\% of the original
training data. These results include an F1 score of 96\% and a False Negative
(FN) error rate of 4\%. Additionally, the average detection time per CT image
on a CPU is 0.62 seconds. Reproducibility: Framework implementation and models
available on GitHub.
