• Sathyavani Addanki and D Sumathi.
• Bratisl Med J. 2024 Jan 1; 125 (3): 196205196-205.

BackgroundDiabetic Retinopathy (DR) is a widespread intense stage of diabetes mellitus that causes vision-effecting anomalies in the retina. It is a medical health condition on the strength of fluctuating glucose level in the blood that can result in vision loss in case of severity.ObjectiveAs a result, early detection and treatment with DR is the most significant task which will tremendously reduce the likelihood of vision impairment and is still a difficult challenge. Many conventional methods fail to detect primary causes of formation of Microaneurysms, that are used to determine the Prediagnosis of DR.MethodTo overcome this challenge, the proposed model incorporates Harris Hawk Optimization with CNN-Bi-LSTM (HHO-CBL) to extract the features. The Prediagnosis of DR has been achieved through this model by spotting saccular dilations, hyaline like material in the capillary aneurysm wall, kinking of vessels since these are the indications for the creation of microaneurysms that are spotted in the blood vessel of the retina. The recommended model is also used to automatically detect DR and its progression in many phases. Furthermore, in order to identify the severity of DR retina, we used a benchmark Kaggle APTOS dataset to train the HHO-CBL model.ResultsExperimental results reveal that this model obtains the best classification accuracy of 96.4 % for an early diagnosis and 98.8 % for a five-degree classification. In addition to those results, a comparison with previously carried out studies has also shown that this model provides a promising solution for a successful Prediagnosis of DR and its staging.ConclusionsIn the current research, an innovative HHO-CBL was developed for identifying the primary causes that lead to the formation of microaneurysms and diagnosing all five grades of DR. According to the acquired results presented through the evaluation performance metrics indicates that the pre-early diagnosis and five grade classification using feature embedding technique outperformed the other prevailing approaches (Tab. 4, Fig. 10, Ref. 31).

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