Rim Beanbonyka, Jang Hyeonung, Lee Hongchang, Jeon Wangsu
Department of Software Convergence, Soonchunhyang University, Asan 31538, Republic of Korea.
Haewootech Co., Ltd., Busan 46742, Republic of Korea.
Bioengineering (Basel). 2025 Jun 9;12(6):630. doi: 10.3390/bioengineering12060630.
Early detection of tuberculosis plays a critical role in effective treatment management. Like active tuberculosis, early identification of inactive forms such as latent or healed tuberculosis is essential to prevent future reactivation. In this study, we developed a deep-learning-based binary classification model to distinguish between active and inactive tuberculosis cases. Our model architecture incorporated an EfficientNet backbone with an MLP-Mixer classification head and was fine-tuned on a dataset annotated by Cheonan Soonchunhyang Hospital. To enhance predictive performance, we applied transfer learning using weights pre-trained on the JFT-300M dataset via the Noisy Student training method. Unlike conventional models, our approach achieved competitive results, with an accuracy of 96.3%, a sensitivity of 95.9%, and a specificity of 96.6% on the test set. These promising outcomes suggest that our model could serve as a valuable asset to support clinical decision-making and streamline early screening workflows for latent tuberculosis.
结核病的早期检测在有效的治疗管理中起着关键作用。与活动性结核病一样，早期识别潜伏性或已治愈等非活动性形式的结核病对于预防未来的复发至关重要。在本研究中，我们开发了一种基于深度学习的二元分类模型，以区分活动性和非活动性结核病例。我们的模型架构采用了带有MLP-Mixer分类头的EfficientNet主干，并在忠南顺天乡医院标注的数据集上进行了微调。为了提高预测性能，我们通过Noisy Student训练方法使用在JFT-300M数据集上预训练的权重进行迁移学习。与传统模型不同，我们的方法取得了具有竞争力的结果，在测试集上的准确率为96.3%，灵敏度为95.9%，特异性为96.6%。这些令人鼓舞的结果表明，我们的模型可以作为一项有价值的资产，以支持临床决策并简化潜伏性结核病的早期筛查工作流程。
