Automated classification of panoramic radiographs with inflammatory periapical lesions using a CNN-LSTM architecture.

Abstract

Objectives: Considering Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network approaches have shown promising image classification performance, the aim of this study was to compare the performance of novel Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) architectures with a classic CNN for classification of panoramic radiographs with inflammatory periapical lesions.

Methods: A dataset of 356 panoramic radiographs with periapical lesions and 769 control images were retrospectively collected and divided into training, validation, and testing sets. Next, four different models were constructed: a classic CNN, a classic LSTM, a cascaded CNN-LSTM, and parallel CNN-LSTM architecture. In each model the CNN took the full panoramic radiograph as input while the LSTM network ran on the images divided into 6 sequential patches. Sensitivity, specificity, and Area Under the Receiver-Operating Curve (AUC) were calculated. McNemar's test compared the sensitivity and specificity between the classic CNN and the other models.

Results: Parallel CNN-LSTM had a significantly higher sensitivity than classic CNN for detecting periapical lesions (95% vs. 81%, 95% confidence interval for the difference = 6 - 22 %, P = 0.002), while also exhibiting the best overall performance of the four models [AUC = 96% vs. 90% (classic CNN), 92% (classic LSTM), and 94% (cascaded CNN-LSTM)].

Conclusions: The parallel CNN-LSTM architecture outperformed the classic CNN for classification of panoramic radiographs with inflammatory periapical lesions.

Clinical significance: Combining CNN and LSTM models improves the classification of panoramic radiographs with and without inflammatory periapical lesions.