Multi-scale information fusion network with label smoothing strategy for corneal ulcer classification in slit lamp images

Abstract

Corneal ulcer is the most common symptom of corneal disease, which is one of the main causes of corneal blindness. The accurate classification of corneal ulcer has important clinical importance for the diagnosis and treatment of the disease. To achieve this, we propose a deep learning method based on multi-scale information fusion and label smoothing strategy. Firstly, the proposed method utilizes the densely connected network (DenseNet121) as backbone for feature extraction. Secondly, to fully integrate the shallow local information and the deep global information and improve the classification accuracy, we develop a multi-scale information fusion network (MIF-Net), which uses multi-scale information for joint learning. Finally, to reduce the influence of the inter-class similarity and intra-class diversity on the feature representation, the learning strategy of label smoothing is introduced. Compared with other state-of-the-art classification networks, the proposed MIF-Net with label smoothing achieves high classification performance, which reaches 87.07 and 83.84% for weighted-average recall (W_R) on the general ulcer pattern and specific ulcer pattern, respectively. The proposed method holds promise for corneal ulcer classification in fluorescein staining slit lamp images, which can assist ophthalmologists in the objective and accurate diagnosis of corneal ulcer.

Detection of Corneal Ulcer Using a Genetic Algorithm-Based Image Selection and Residual Neural Network

“…The segmentation (5) and the medical (3), a total of 8 studies were excluded due to being not focused on the classification. In the remaining 5 studies, corneal ulcer types, which are point-like corneal, point-flaky mixed corneal and flaky corneal ulcers, has been aimed to classify without detecting corneal ulcer using the transfer learning [ 60 , 61 , 62 , 63 , 64 ]. The details of the mentioned publications [ 60 , 61 , 62 , 63 , 64 ] are presented in Table 6 .…”

“…In the remaining 5 studies, corneal ulcer types, which are point-like corneal, point-flaky mixed corneal and flaky corneal ulcers, has been aimed to classify without detecting corneal ulcer using the transfer learning [ 60 , 61 , 62 , 63 , 64 ]. The details of the mentioned publications [ 60 , 61 , 62 , 63 , 64 ] are presented in Table 6 . The binary classification s being corneal ulcer or not being has been aimed a in our study.…”

An Accurate Classification of Rice Diseases Based on ICAI-V4

“…If these images are directly used for training without proper preprocessing, the network's ability to extract rice disease features is greatly weakened, which ultimately leads to low recognition accuracy. (2) Rice exhibits large intraclass diversity and high interclass similarity [15], evident in diseases such as blast and brown spot. Traditional convolutional neural networks struggle with accurate differentiation, thus requiring improvements in the network model to enhance its feature extraction capabilities [16].…”