Transfer Learning for Effective Urolithiasis Detection
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Purpose: Urolithiasis is a common disease that can cause acute pain and complications. The objective of this study was to develop a deep learning model utilizing transfer learning for the rapid and accurate detection of urinary tract stones. By employing this method, we aim to improve the efficiency of medical staff and contribute to the progress of deep learning-based medical image diagnostic technology. Methods: The ResNet50 model was employed to develop feature extractors for detecting urinary tract stones. Transfer learning was applied by utilizing the weights of pretrained models as initial values, and the models were fine-tuned with the provided data. The model’s performance was evaluated using accuracy, precision-recall, and receiver operating characteristic curve metrics. Results: The ResNet-50-based deep learning model demonstrated high accuracy and sensitivity, outperforming traditional methods. Specifically, it enabled a rapid diagnosis of the presence or absence of urinary tract stones, thereby assisting doctors in their decision-making process. Conclusions: This research makes a meaningful contribution by accelerating the clinical implementation of urinary tract stone detection technology utilizing ResNet-50. The deep learning model can swiftly identify the presence or absence of urinary tract stones, thereby enhancing the efficiency of medical staff. We expect that this study will contribute to the advancement of medical imaging diagnostic technology based on deep learning.
Abstract
Purpose: Urolithiasis is a common disease that can cause acute pain and complications. The objective of this study was to develop a deep learning model utilizing transfer learning for the rapid and accurate detection of urinary tract stones. By employing this method, we aim to improve the efficiency of medical staff and contribute to the progress of deep learning-based medical image diagnostic technology. Methods: The ResNet50 model was employed to develop feature extractors for detecting urinary tract stones. Transfer learning was applied by utilizing the weights of pretrained models as initial values, and the models were fine-tuned with the provided data. The model’s performance was evaluated using accuracy, precision-recall, and receiver operating characteristic curve metrics. Results: The ResNet-50-based deep learning model demonstrated high accuracy and sensitivity, outperforming traditional methods. Specifically, it enabled a rapid diagnosis of the presence or absence of urinary tract stones, thereby assisting doctors in their decision-making process. Conclusions: This research makes a meaningful contribution by accelerating the clinical implementation of urinary tract stone detection technology utilizing ResNet-50. The deep learning model can swiftly identify the presence or absence of urinary tract stones, thereby enhancing the efficiency of medical staff. We expect that this study will contribute to the advancement of medical imaging diagnostic technology based on deep learning.
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