Abstract Detecting kidney stones in CT images presents significant challenges due to variations in stone size, shape, intensity, and their similarity to surrounding tissues. Traditional methods often struggle with false positives and missed detections, especially in complex or noisy scan environments. To address these issues, we propose a novel deep learning architecture that combines advanced feature extraction, attention mechanisms, and multi-scale fusion strategies. The model incorporates the REPNCSPELAN4 block to capture diverse spatial and channel-wise features, followed by an ADown module for aggressive down sampling, enabling deeper semantic understanding with efficient computation. The SPEELAN block introduces spatial and channel attention to highlight diagnostically relevant regions, while the CBFuse module performs cross-block fusion, integrating fine-grained details with high-level context for improved multi-scale detection. Experimental evaluations demonstrate that the proposed model achieves a precision of 0.798, recall of 0.742, and mAP of 0.795, showing its effectiveness and robustness in accurately detecting kidney stones across diverse CT scenarios.
Sri et al. (Wed,) studied this question.
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