H1DBi-R Net: Hybrid 1D Bidirectional RNN for Efficient Diabetic Retinopathy Detection and Classification

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Nowadays the eye disease that widely affects the visual impairment of humans is Diabetes Retinopathy (DR). The advanced stage of the disorder leads to cause complete vision loss and creates complex situations for treatment. So it is significant to treat prolonged diabetes at an initial stage. Therefore, the main reason for DR is the uncontrolled growth of blood glucose levels in the eye. If it reaches the severity level the bleeding is caused in the eye. However, the lesions generated due to DR are medicated based on fundus images. The significant purpose of affecting DR is the presence of high sugar in the blood and this damages the retina. Therefore proper screening of DR is essential to prevent it from affecting the blood vessels all over the body. Also, it unblocks blood vessels paves the way to function the new blood vessels grown in the eye. Therefore a novel hybrid oppositional fire-fly modified 1D bidirectional recurrent (HOF-M1DBR) method is proposed to detect the DR through fundus images accurately. The datasets Messidor-1 and APTOS-2019 are applied for performing the initial process of fundus images such as denoising, smoothing, cropping, and resizing. The M1DBR is used to validate the accuracy and optimize the weight function using the Opposition-Based Learning-FireFly (OBL-FF) algorithm. Thus prediction and classification of f DR from the fundus images are detected accurately and distinguished the four levels from the extracted features. The validation of the proposed method is performed based on accuracy, precision, recall, and F1-score. The experimental results revealed that the proposed HOF-M1DBR attained an accuracy of 98.9% for the Messidor-1 and APTOS-2019 dataset, thus improving the performance. However, the existing DCNN-PCA-FF, DNN-MSO, SI-GWO, and DCNN-EMF methods diminished the performance by 79.9%, 87%, 83%, and 89.1% respectively.

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Artificial Intelligence Review



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