Automatic computer vision-based detection and quantitative analysis of indicative parameters for grading of diabetic retinopathy

Deep feed forward neural network-based screening system for diabetic retinopathy severity classification using the lion optimization algorithm

Diabetic Retinopathy (DR) has become a major cause of blindness in recent years. Diabetic patients should be screened on a regular basis for early detection, which can help them avoid blindness. Furthermore, the number of diabetic patients undergoing these screening procedures is rapidly increasing, resulting in increased workload for ophthalmologists. An efficient screening system that assists ophthalmologists in DR diagnosis saves ophthalmologists a lot of time and effort. To address this issue, an automatic DR detection screening system is required to improve diagnosis speed and detection accuracy. Appropriate treatment can be provided to patients to prevent vision loss if the severity levels of DR are accurately diagnosed in the early stages. A growing number of screening systems for DR diagnosis have been developed in recent years using various deep learning models, and the majority of the published work did not include any optimization algorithm in the neural network for severity classification. The use of an optimization algorithm with the necessary hyper parameter tuning will improve the model's performance. Considering this as motivation, we proposed a five-phase DFNN-LOA model. The DFNN-LOA algorithm presented here has five phases: (i) pre-processing, (ii) optic disc detection, (iii) segmentation, (iv) feature extraction, and (v) severity classification. The proposed model's experimental analysis is carried out on the MESSIDOR dataset. The experimental results show that the proposed DFNN-LOA model has superior characteristics, with maximum accuracy, sensitivity, specificity, F1-score, PPV, and NPV of 97.6%, 98.4%, 90.7%, 96.5%, 94.6%, and 97.1%, respectively.