An enumerative pre-processing approach for retinopathy severity grading using an interpretable classifier: a comparative study.

BACKGROUND: Diabetic retinopathy (DR) is a serious eye complication that results in permanent vision damage. As the number of patients suffering from DR increases, so does the delay in treatment for DR diagnosis. To bridge this gap, an efficient DR screening system that assists clinicians is required. Although many artificial intelligence (AI) screening systems have been deployed in recent years, accuracy remains a metric that can be improved. METHODS: An enumerative pre-processing approach is implemented in the deep learning model to attain better accuracies for DR severity grading. The proposed approach is compared with various pre-trained models, and the necessary performance metrics were tabulated. This paper also presents the comparative analysis of various optimization algorithms that are utilized in the deep network model, and the results were outlined. RESULTS: The experimental results are carried out on the MESSIDOR dataset to assess the performance. The experimental results show that an enumerative pipeline combination K1-K2-K3-DFNN-LOA shows better results when compared with other combinations. When compared with various optimization algorithms and pre-trained models, the proposed model has better performance with maximum accuracy, precision, recall, F1 score, and macro-averaged metric of 97.60%, 94.60%, 98.40%, 94.60%, and 0.97, respectively. CONCLUSION: This study focussed on developing and implementing a DR screening system on color fundus photographs. This artificial intelligence-based system offers the possibility to enhance the efficacy and approachability of DR diagnosis.

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.