Deep learning-based system for automatic identification of benign and malignant eyelid tumours.

Abstract

AIMS Our aim is to develop a deep learning-based system for automatically identifying and classifying benign and malignant tumours of the eyelid to improve diagnostic accuracy and efficiency. METHODS The dataset includes photographs of normal eyelids, benign and malignant eyelid tumours and was randomly divided into a training and validation dataset in a ratio of 8:2. We used the training dataset to train eight convolutional neural network models to classify normal eyelids, benign and malignant eyelid tumours. These models included VGG16, ResNet50, Inception-v4, EfficientNet-V2-M and their variants. The validation dataset was used to evaluate and compare the performance of the different deep learning models. RESULTS All eight models achieved an average accuracy greater than 0.746 for identifying normal eyelids, benign and malignant eyelid tumours, with an average sensitivity and specificity exceeding 0.790 and 0.866, respectively. The mean area under the receiver operating characteristic curve (AUC) for the eight models was more than 0.904 in correctly identifying normal eyelids, benign and malignant eyelid tumours. The dual-path Inception-v4 network demonstrated the highest performance, with an AUC of 0.930 (95% CI 0.900 to 0.954) and an F1-score of 0.838 (95% CI 0.787 to 0.882). CONCLUSION The deep learning-based system shows significant potential in improving the diagnosis of eyelid tumours, providing a reliable and efficient tool for clinical practice. Future work will validate the model with more extensive and diverse datasets and integrate it into clinical workflows for real-time diagnostic support.