Thyroid Cancer Computer-Aided Diagnosis System using MRI-Based Multi-Input CNN Model

Abstract

Achieving early detection and classification of thyroid nodules contributes to the prediction of cancer burdening and also steers appropriate clinical pathways of that medical condition. We propose a novel multimodal MRI-based computer-aided diagnosis (CAD) system that detects cancerous thyroid nodules using a deep-learning architecture. Particularly, our system is built with a multi-input convolutional neural network (CNN) to perform fusion of two MRI modalities: the diffusion weighted image (DWI) and apparent diffusion coefficient (ADC) map. The main contribution of our system is three-folded. Namely, (1) it is the first system to fuse thyroid DWI and ADC using CNN for classification purpose; (2) it enables independent convolutions process for each of DWI and ADC images, which can increase the likelihood of detecting deep texture patterns in thyroid nodules; and (3) it enables adding extra channels in each input with the possibility to integrate with additional MRI modalities and other imaging technologies. We compared our system to other fusion methods and also to other machine learning (ML) frameworks that use hand-crafted features. Our system achieved the highest performance among them with diagnostic accuracy of 0.88, precision of 0.82, and recall of 0.82.