An ensemble of deep learning models with falcon optimization assisted diabetic retinopathy diagnosis on retinal fundus images

Diabetic Retinopathy (DR) is a prominent results of diabetes mellitus that causes abnormalities lesions in retina. If not identified at early, it may progress to complete loss of vision. Unfortunately, DR is an irreversible, and treatment only sustains existing vision. Timely detection and accurate treatment of DR can considerably decrease the chance of blindness. Manual diagnosis of DR in retinal fundus images (RFIs) by ophthalmologist is time consuming, costly and laborious tasks with a higher risk of misdiagnosis. Recently, Deep learning (DL) has gained popularity and shown remarkable performance particularly in medical image analysis and classification. Convolutional neural networks (CNNs) are increasingly being used as a DL approach in medical image analysis, and they are very efficient. This manuscript offers the design of Falcon Optimizer with Ensemble of Deep Learning Algorithm Assisted Diabetic Retinopathy Diagnosis Model (FOEDLA-DRDM) system on RFIs. The FOEDLA-DRDM system employs a Wiener filtering (WF) based preprocessing approach to eliminate noise from images. Following this, FOEDLA-DRDM system leverages the SE-DenseNet method to generate the feature vectors. For DR recognition FOEDLA-DRDM system applies an ensemble approach that combines - AutoEncoder, long short-term memory (LSTM), and deep belief network (DBN). Finally, Falcon Optimizer (FO) adjusts the hyperparameter values of the ensemble approach, giving rise to classification efficiency. The FOEDLA-DRDM system is validated by simulating it on a Kaggle DR dataset, with results being measured according to various criteria. The simulation findings showcase the effectiveness of the FOEDLA-DRDM system in diagnosis of DR.