What question did this study set out to answer?

The aim is to accurately classify Kikuchi disease, lymphoma, lymphadenitis, and tuberculosis using convolutional neural networks from cervical CT images.

May 15, 2026Open Access

Convolutional neural network analysis of cervical CT images: classification of Kikuchi disease, lymphoma, lymphadenitis, and tuberculosis

Key Points

The aim is to accurately classify Kikuchi disease, lymphoma, lymphadenitis, and tuberculosis using convolutional neural networks from cervical CT images.
Implemented six CNN models for single and multiple disease classifications.
Calculated performance metrics including accuracy, precision, recall, and F1-score.
Evaluated models on a test dataset with varying performance across diseases.
Model for lymphadenitis achieved precision of 0.920 with 95% CI and p-value not stated.
Overall accuracy of models ranged from 0.6864 to 0.9278 with F1-scores between 0.5310 and 0.8710.
The CNN-based technique serves as an efficient pre-screening tool, though clinical applicability requires further enhancement.

Abstract

Kikuchi disease, lymphoma, lymphadenitis, and tuberculosis are common diseases affecting the head and neck. The causes and treatment methods differ; however, the initial symptoms of these diseases (fever, pain, and neck swelling) are generally similar; therefore, it is important to accurately determine the type of disease at its initial stage. During the performance evaluation, the values of precision-recall area under the curve (PR AUC) were 0.785, 0.731, 0.920, and 0.821 for the four single-disease detection models for Kikuchi disease, lymphoma, lymphadenitis, and tuberculosis, respectively; 0.819 for a model for classifying the type of treatment; and 0.807 for a model for classifying all diseases with a single inspection. In the model performance tests, all six implemented models showed relatively high performance for the test dataset (accuracy: 0.6864–0.9278, precision: 0.7532–0.9583, recall: 0.3896–0.8804, and F1-score: 0.5310–0.8710). Based on these experimental results, we conclude that the proposed CNN-based diagnosis support technique has the potential to be an efficient pre-screening tool for first-time patients with Kikuchi disease, lymphoma, lymphadenitis, and tuberculosis by reducing the dependency on high-risk invasive diagnosis; however, additional model enhancement is required to improve its clinical applicability.

Demander à l'IA

Bookmark

View Full Paper