Thyroid Cancer Diagnostic System using Magnetic Resonance Imaging

Abstract

Early detection and diagnosis of thyroid nodules are very important to rescue patients before the cancer spreads all over the patient's body. A computer-aided diagnosis (CAD) system is proposed to detect the malignancy of thyroid nodules using magnetic resonance imaging (MRI) scans. This system extracts three descriptive features from T2-weighted (T2) MRI. These features are 1st-order reflectivity, 2nd-order reflectivity, and spherical harmonic. The 1st-order reflectivity is represented by sufficient statistics, (i.e. CDF percentiles), extracted from the cumulative distribution function (CDF) generated from it. After-ward, these features are fed to a neural network (NN) individually for diagnosis. Then, the classification outputs for these networks are fused using another NN for final diagnosis. The developed system is trained and tested using leave-one-subject-out (LOSO) cross-validation technique on MRI scans from 63 patients. The proposed fusion system shows incredible improvements in diagnostic accuracy, compared with other machine learning approach and a well-know pretrained deep learning network as well as individual feature classification. The overall sensitivity, specificity, F1-score, and accuracy of the proposed system are 91.3%, 95%, 91.3%, and 93.65%, respectively. The reported results, based on the fusion of reflectivity features as well as morphological feature, show the promise of the developed system in differentiating between benign and malignant thyroid nodules. © 2022 IEEE. Keywords Computer-Aided Diagnosis (CAD), Gray-Level Co-occurrence Matrix (GLCM), Magnetic Resonance Imaging (MRI),