翼状胬肉和结膜下出血是两种常见的眼表疾病,可引起患者痛苦和焦虑。在这项研究中,收集了2855张眼表图像,分为四类:正常眼表,结膜下出血,要观察的翼状胬肉,翼状胬肉需要手术治疗.我们提出了一种眼表疾病的诊断分类模型,PFM块加固的双分支网络(DBPF-Net),采用具有两分支结构特性的构象模型作为眼表疾病四向分类模型的骨干。此外,我们提出了一个由补丁合并层和FReLU层组成的块(PFM块)用于提取空间结构特征,以进一步增强模型的特征提取能力。在实践中,只需要将眼表图像输入到模型中以自动区分疾病类别。我们还训练了VGG16、ResNet50、EfficientNetB7和Conformer模型,并对测试集上所有模型的结果进行评估和分析。主要评价指标为灵敏度,特异性,F1分数,接收器工作特性曲线下面积(AUC),卡帕系数,和准确性。在几个实验中,所提出的诊断模型的准确性和κ系数分别平均为0.9789和0.9681。敏感性,特异性,F1分数,AUC是,分别,0.9723、0.9836、0.9688和0.9869用于诊断翼状胬肉。and,分别,0.9210、0.9905、0.9292和0.9776用于诊断需要手术的翼状胬肉。该方法对识别这四种类型的眼表图像具有较高的临床参考价值。
Pterygium and subconjunctival hemorrhage are two common types of ocular surface diseases that can cause distress and anxiety in patients. In this study, 2855 ocular surface images were collected in four categories: normal ocular surface, subconjunctival hemorrhage, pterygium to be observed, and pterygium requiring surgery. We propose a diagnostic classification model for ocular surface diseases, dual-branch network reinforced by PFM block (DBPF-Net), which adopts the conformer model with two-branch architectural properties as the backbone of a four-way classification model for ocular surface diseases. In addition, we propose a block composed of a patch merging layer and a FReLU layer (PFM block) for extracting spatial structure features to further strengthen the feature extraction capability of the model. In practice, only the ocular surface images need to be input into the model to discriminate automatically between the disease categories. We also trained the VGG16, ResNet50, EfficientNetB7, and Conformer models, and evaluated and analyzed the results of all models on the test set. The main evaluation indicators were sensitivity, specificity, F1-score, area under the receiver operating characteristics curve (AUC), kappa coefficient, and accuracy. The accuracy and kappa coefficient of the proposed diagnostic model in several experiments were averaged at 0.9789 and 0.9681, respectively. The sensitivity, specificity, F1-score, and AUC were, respectively, 0.9723, 0.9836, 0.9688, and 0.9869 for diagnosing pterygium to be observed, and, respectively, 0.9210, 0.9905, 0.9292, and 0.9776 for diagnosing pterygium requiring surgery. The proposed method has high clinical reference value for recognizing these four types of ocular surface images.