Optimized YOLOv5 Architecture for Superior Kidney Stone Detection in CT Scans

The early and accurate detection of kidney stones is crucial for effective treatment and improved patient outcomes. This paper proposes a novel modification of the YOLOv5 model, specifically tailored for detecting kidney stones in CT images. Our approach integrates the squeeze-and-excitation (SE) block within the C3 block of the YOLOv5m architecture, thereby enhancing the ability of the model to recalibrate channel-wise dependencies and capture intricate feature relationships. This modification leads to significant improvements in the detection accuracy and reliability. Extensive experiments were conducted to evaluate the performance of the proposed model against standard YOLOv5 variants (nano-sized, small, and medium-sized). The results demonstrate that our model achieves superior performance metrics, including higher precision, recall, and mean average precision (mAP), while maintaining a balanced inference speed and model size suitable for real-time applications. The proposed methodology incorporates advanced noise reduction and data augmentation techniques to ensure the preservation of critical features and enhance the robustness of the training dataset. Additionally, a novel color-coding scheme for bounding boxes improves the clarity and differentiation of the detected stones, facilitating better analysis and understanding of the detection results. Our comprehensive evaluation using essential metrics, such as precision, recall, mAP, and intersection over union (IoU), underscores the efficacy of the proposed model for detecting kidney stones. The modified YOLOv5 model offers a robust, accurate, and efficient solution for medical imaging applications and represents a significant advancement in computer-aided diagnosis and kidney stone detection.