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

British Journal of Ophthalmology, vol. 109, pp. 1050–1055

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.

Authors 4

1. Wanlin Fan

   University of Cologne · University Hospital Cologne

   Affiliation as printed

   Department of Ophthalmology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany

2. Martine Johanna Jager Aachen Department of Ophthalmology, Leiden University Medical Center

   Leiden University Medical Center

   Affiliation as printed

   Department of Ophthalmology, Leiden University Medical Center, Leiden, The Netherlands

3. Weiwei Dai

   Aier Eye Hospital Group (China)

   Affiliation as printed

   Changsha Aier Eye Hospital, Hunan, China

   Changsha Aier Eye Hospital, Hunan, China ludwig.heindl@uk-koeln.de daiweiwei@aierchina.com

4. Ludwig Maximilian Heindl

   University of Cologne · University Hospital Cologne

   Affiliation as printed

   Center for Integrated Oncology (CIO), Aachen-Bonn-Cologne-Duesseldorf, Cologne, Germany

   Department of Ophthalmology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany

   Department of Ophthalmology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany ludwig.heindl@uk-koeln.de daiweiwei@aierchina.com