Deep learning-based malignancy prediction in thyroid nodules

Although the vast majority of thyroid nodules are non-cancerous, determining whether or not a nodule is cancerous may be a difficult and time-consuming process that often involves unnecessary surgical events. In addition, we discussed the process of developing a model that might anticipate the presence of cancer in thyroid nodules by including a number of the core demographic and ultrasound parameters. A combined sensitivity and specificity score was used to assess the diagnostic performance, and their accuracy was compared to that of radiologists. The comparison between model prediction and expert evaluation reveals the benefit of our approach over human judgement in predicting thyroid nodule malignancy. The results of the experiments show that the suggested algorithm performs better. Nodules of TI-RADS category 4 were used. The area under the receiver operating characteristic curve in the validation dataset was 0.92 (with accuracy of 0.70, sensitivity of 0.81 and specificity of 0.58).