aspg-18-3718-1792025575 20261015005255 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2025 16 2 Arwa Darwish Alzughaibi Applied College, Taibah University, Medina, Saudi Arabia Ashrf Althbiti Department of Information Technology, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia Sultan Ahmed Almalki Computer Department, Applied College, Najran University, Najran 61441, Saudi Arabia Mohammed Al-Jabbar Computer Department, Applied College, Najran University, Najran 61441, Saudi Arabia Mohammed Alshahrani Computer Department, Applied College, Najran University, Najran 61441, Saudi Arabia Diabetic Retinopathy (DR) is a general difficulty of diabetes mellitus, resulting in retina damage that affects vision. If left undetected, it has the potential to cause blindness. Regrettably, DR is irreversible, and only treatment can maintain vision. The early analysis and treatment of DR can considerably decrease the potential for visual impairment. Unlike computer-aided diagnosis (CAD) systems, the manual diagnostics method of DR retinal images by ophthalmologists is effort-, cost-, and time-consuming and liable to misdiagnoses. In present scenario, deep learning (DL) has become the classical approach that has remarkable performance in different fields, mainly in medical image classification and analysis. Convolutional neural networks (CNN) are more commonly deployed as a DL system in medical image analysis and they are very efficient. In this manuscript, we offer the design of Tent Chaotic Dung Beetle Optimization with Deep Ensemble Learning for Diabetic Retinopathy (TCDBO-DELDR) Recognition approach on Fundus Imaging. The foremost intention of the TCDBO-DELDR technique is to automate the DR detection process on fundus images via the ensemble DL model. To eradicate the noise, the TCDBO-DELDR technique initially exploits the median filtering (MF) methodology. In the TCDBO-DELDR model, the Inception v3 (IV3) model is employed for the purposes of feature extractor. For the hyperparameter tuning procedure, the TCDBO technique is used for IV3 model. Finally, the detection of DR is carried out utilizing an ensemble of three classifiers namely Deep Feedforward Neural Network (DeepFFNN), Convolutional FFNN (ConvFFNN), and Convolutional bi-directional long short-term memory (ConvBLSTM). For ensuring the enhanced efficiency of the TCDBO-DELDR system in the DR detection procedure, a widespread experimental study is prepared on the benchmark DR database. The results illustrate the superior efficiency of the TCDBO-DELDR technique with other recent DL approaches. 2025 158 173 3718 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.160212 https://americaspg.com/journal/18/article/3718 https://americaspg.com/storage/01742089937.pdf