Multi-channel deep convolutional neural networks for multi-classifying thyroid disease

TL;DR

This paper introduces a novel multi-channel CNN architecture for multi-class thyroid disease classification, improving diagnostic accuracy and handling co-existing disease types using CT images.

Contribution

The study proposes a multi-channel CNN model that enhances multi-class thyroid disease diagnosis and co-existence detection, outperforming standard single-channel CNNs.

Findings

Achieved 0.909 accuracy, 0.944 precision, 0.896 recall, 0.994 specificity, and 0.917 F1 score.

Model showed consistent performance across gender groups with over 0.90 accuracy.

Demonstrated improved performance over traditional single-channel CNN architecture.

Abstract

Thyroid disease instances have been continuously increasing since the 1990s, and thyroid cancer has become the most rapidly rising disease among all the malignancies in recent years. Most existing studies focused on applying deep convolutional neural networks for detecting thyroid cancer. Despite their satisfactory performance on binary classification tasks, limited studies have explored multi-class classification of thyroid disease types; much less is known of the diagnosis of co-existence situation for different types of thyroid diseases. Therefore, this study proposed a novel multi-channel convolutional neural network (CNN) architecture to address the multi-class classification task of thyroid disease. The multi-channel CNN merits from computed tomography to drive a comprehensive diagnostic decision for the overall thyroid gland, emphasizing the disease co-existence circumstance. Moreover, this study also examined alternative strategies to enhance the diagnostic accuracy of CNN models through concatenation of different scales of feature maps. Benchmarking experiments demonstrate the improved performance of the proposed multi-channel CNN architecture compared with the standard single-channel CNN architecture. More specifically, the multi-channel CNN achieved an accuracy of 0.909, precision of 0.944, recall of 0.896, specificity of 0.994, and F1 of 0.917, in contrast to the single-channel CNN, which obtained 0.902, 0.892, 0.909, 0.993, 0.898, respectively. In addition, the proposed model was evaluated in different gender groups; it reached a diagnostic accuracy of 0.908 for the female group and 0.901 for the male group. Collectively, the results highlight that the proposed multi-channel CNN has excellent generalization and has the potential to be deployed to provide computational decision support in clinical settings.