Hybrid Deep Learning Model for Cataract Diagnosis Assistance - Zonghong Feng; Kai Xu; Liangchang Li; Yong Wang (Applied Sciences)
With the population aging globally, cataracts have become one of the main causes of vision impairment. Early diagnosis and treatment of cataracts are crucial for preventing blindness. However, the use of deep learning models for assisting in the diagnosis of cataracts is limited due to reasons such as scarce data labeling, small sample size, uneven distribution, and poor generalization ability in the field. Therefore, this paper proposes a hybrid deep learning network for assisting in the diagnosis of cataract fundus images, attempting to solve the above problems and limitations. The network is based on the idea of transfer learning for feature extraction of fundus images, and introduces the Squeeze-and-Excitation (SE) module and prototype network for feature enhancement and classification, improving the model’s generalization ability for new disease samples. Finally, this paper verifies the role of each part of the model through ablation experiments, especially the significant contribution of the SE_block module and the prototype network classifier in enhancing the model’s performance. The experimental results show that the proposed model achieves excellent performance in the task of cataract fundus image recognition, with an accuracy of 0.9875, AUC value of 0.9984, and F1 score of 0.9855. The establishment of this hybrid model not only provides an effective tool for the auxiliary diagnosis of cataracts but also provides a new perspective and method for the application of deep learning in the field of ophthalmic disease recognition.