RR‐HCL‐SVM: A two‐stage framework for assessing remaining thyroid tissue post‐thyroidectomy in SPECT images

Abstract

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.