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

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.

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.

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)].

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

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