Lisha L B, R Sylaja Vallee Narayan S
Department of Computer Science and Engineering, Ponjesly College of Engineering, Alamparai, Parvathipuram, Nagercoil, Kanyakumari, Tamil Nadu, 629003, India.
Department of Computer Science and Engineering, GITAM School of Technology, GITAM University, Bengaluru, Karnataka, 561203, India.
Int Ophthalmol. 2025 Aug 5;45(1):324. doi: 10.1007/s10792-025-03660-w.
Ophthalmologists use retinal fundus imaging as a useful tool to diagnose retinal issues. Recently, research on machine learning has concentrated on disease diagnosis. However, disease detection is less accurate, more likely to be misidentified, and often takes a long time to get the right conclusions. This study suggested a new hybrid Deep Learning (DL) approach for retinal illness classification using retinal images to overcome these problems. Three crucial stages are included in this proposed study: preprocessing, feature extraction, and disease classification.
At first, the retinal images are preprocessed using the Modified Gaussian Filtering technique to enhance the quality of the image. Subsequently, ResNet, VGG16-based feature descriptors are applied to the preprocessed image along with Improved Multi-Texton features, and statistical features are derived to obtain the most pertinent characteristics and minimize the dimensionality to boost the performance of the model. Then, these obtained features are employed in the hybrid classification model, which is a combination of an Improved LinkNet (ILinkNet) and SqueezeNet models. These models independently process the features for effective classification of disease. Lastly, the final classification results are obtained by averaging the outcomes of both classifiers.
Additionally, the efficiency of the proposed ILink-SqNet model is assessed in comparison to the current techniques. As a result, the ILink-SqNet model achieved a precision of 0.951, which surpasses the result of MobileNet (0.846), SpinalNet (0.821), CNN-Trans (0.836), and LinkNet (0.859), SqueezeNet (0.794) and Fundus-DeepNet (0.762) respectively.
Therefore, the suggested ILink-SqNet method provides a robust and effective solution for disease classification, ultimately contributing to better patient outcomes and more efficient clinical practices.
眼科医生将视网膜眼底成像作为诊断视网膜问题的有用工具。最近，机器学习研究集中在疾病诊断上。然而，疾病检测准确性较低，更容易被误诊，并且往往需要很长时间才能得出正确结论。本研究提出了一种新的混合深度学习（DL）方法，用于使用视网膜图像进行视网膜疾病分类，以克服这些问题。本拟议研究包括三个关键阶段：预处理、特征提取和疾病分类。
首先，使用改进的高斯滤波技术对视网膜图像进行预处理，以提高图像质量。随后，将基于ResNet、VGG16的特征描述符与改进的多纹理特征一起应用于预处理后的图像，并导出统计特征以获得最相关的特征并最小化维度，以提高模型的性能。然后，将这些获得的特征应用于混合分类模型，该模型是改进的LinkNet（ILinkNet）和SqueezeNet模型的组合。这些模型独立处理特征以有效分类疾病。最后，通过平均两个分类器的结果获得最终分类结果。
此外，与当前技术相比，评估了所提出的ILink-SqNet模型的效率。结果，ILink-SqNet模型的精度达到0.951，分别超过了MobileNet（0.846）、SpinalNet（0.821）、CNN-Trans（0.836）、LinkNet（0.859）、SqueezeNet（0.794）和Fundus-DeepNet（0.762）的结果。
因此，所建议的ILink-SqNet方法为疾病分类提供了一种强大而有效的解决方案，最终有助于改善患者预后并提高临床实践效率。
