DCT-Net: An effective method to diagnose retinal tears from B-scan ultrasound images

• Published in: Mathematical biosciences and engineering : MBE Vol. 21; no. 1; pp. 1110 - 1124
• Main Authors: Li, Ke, Zhu, Qiaolin, Wu, Jianzhang, Ding, Juntao, Liu, Bo, Zhu, Xixi, Lin, Shishi, Yan, Wentao, Li, Wulan
• Format: Journal Article
• Language: English
• Published: United States AIMS Press 01.01.2024
• Subjects: attention rollout; automatic diagnosis; Electric Power Supplies; Humans; Neuroimaging; Retinal Perforations; retinal tears; Ultrasonography; ultrasound image; vision transformer; automatic diagnosis; attention rollout; ultrasound image; retinal tears; vision transformer
• DOI: 10.3934/mbe.2024046
• ISSN: 1551-0018

Retinal tears (RTs) are usually detected by B-scan ultrasound images, particularly for individuals with complex eye conditions. However, traditional manual techniques for reading ultrasound images have the potential to overlook or inaccurately diagnose conditions. Thus, the development of rapid and accurate approaches for the diagnosis of an RT is highly important and urgent. The present study introduces a novel hybrid deep-learning model called DCT-Net to enable the automatic and precise diagnosis of RTs. The implemented model utilizes a vision transformer as the backbone and feature extractor. Additionally, in order to accommodate the edge characteristics of the lesion areas, a novel module called the residual deformable convolution has been incorporated. Furthermore, normalization is employed to mitigate the issue of overfitting and, a Softmax layer has been included to achieve the final classification following the acquisition of the global and local representations. The study was conducted by using both our proprietary dataset and a publicly available dataset. In addition, interpretability of the trained model was assessed by generating attention maps using the attention rollout approach. On the private dataset, the model demonstrated a high level of performance, with an accuracy of 97.78%, precision of 97.34%, recall rate of 97.13%, and an F1 score of 0.9682. On the other hand, the model developed by using the public funds image dataset demonstrated an accuracy of 83.82%, a sensitivity of 82.69% and a specificity of 82.40%. The findings, therefore present a novel framework for the diagnosis of RTs that is characterized by a high degree of efficiency, accuracy and interpretability. Accordingly, the technology exhibits considerable promise and has the potential to serve as a reliable tool for ophthalmologists.