Retinal image-based disease classification using hybrid deep architecture with improved image features

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.