This paper presents a two‐stage deep learning framework, RR‐HCL‐SVM, designed to aid in the assessment of residual thyroid tissues following thyroidectomy, utilizing single‐photon emission computed tomography (SPECT) images. Leveraging the power of deep learning, our model offers a comprehensive solution for the detection and assessment of remaining thyroid tissues. To enhance accuracy, we introduce a unique combination of features, incorporating the Radio Scan Index (RSI) and radiomics features. These features not only improve accuracy but also provide valuable insights into tissue characterization. Moreover, we employ efficient clustering techniques for feature dimension reduction, preserving model performance while reducing computational complexity. Experimental results demonstrate the effectiveness of our approach, achieving an impressive F1 score of 0.97, sensitivity of 0.96, and specificity of 0.98. The RR‐HCL SVM framework holds great promise in the clinical setting for the precise evaluation of residual thyroid tissues post‐thyroidectomy, offering potential benefits for patient care and treatment planning.

中文翻译：

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RR-HCL-SVM：用于评估 SPECT 图像中甲状腺切除术后剩余甲状腺组织的两阶段框架

本文提出了一个两阶段深度学习框架 RR-HCL-SVM，旨在利用单光子发射计算机断层扫描 (SPECT) 图像帮助评估甲状腺切除术后残留的甲状腺组织。利用深度学习的力量，我们的模型为剩余甲状腺组织的检测和评估提供了全面的解决方案。为了提高准确性，我们引入了独特的功能组合，结合了无线电扫描指数 (RSI) 和放射组学功能。这些功能不仅提高了准确性，而且还为组织表征提供了宝贵的见解。此外，我们采用高效的聚类技术来减少特征维度，在保持模型性能的同时降低计算复杂度。实验结果证明了我们方法的有效性，取得了令人印象深刻的 F1 分数 0.97、敏感性 0.96 和特异性 0.98。 RR-HCL SVM 框架在临床环境中对甲状腺切除术后残留甲状腺组织的精确评估具有广阔的前景，为患者护理和治疗计划提供潜在的好处。