Optimizing Deep Learning Architectures for Diabetic Retinopathy Screening: A Comparative Study between Differential Evolution and Genetic Algorithm
Keywords:
Genetic Algorithm, Deep Learning, Optimization, Diabetic RetinopathyAbstract
Due to its potential to shield diabetic patients' vision from loss, the identification of diabetic retinopathy has taken on important significance in the field of medical diagnostics. This work compares the effectiveness of Differential Evolution (DE) and Genetic Algorithm (GA), two well-known evolutionary algorithms, to optimize deep learning architectures for diabetic retinopathy screening.The main goal of this study is to optimise the architecture of deep learning models in order to improve their performance in identifying diabetic retinopathy. Convolutional neural networks (CNNs), in particular, have demonstrated promise in effectively identifying retinal pictures for illness diagnosis. However, creating an ideal architecture for such networks can be difficult and costly in terms of computing.We used sophisticated optimisation methods DE and GA, both of which are well-known for their capacity to optimise neural network topologies, to deal with this problem. We thoroughly assessed the efficacy of DE and GA in optimising the hyperparameters of CNNs for the detection of diabetic retinopathy. The paper offer important new understandings of the advantages and disadvantages of DE and GA in this particular medicinal application. We evaluated the optimised models' precision, sensitivity, specificity, and computational effectiveness to determine which approach produced the best outcomes. Additionally, we took into account elements like scalability and convergence speed, which are essential for actual deployment in clinical settings.The findings of this study offer insightful advice for academics and professionals looking to increase the diagnostic precision of diabetic retinopathy screening through deep learning methods. We seek to contribute to the creation of more efficient and effective methods for early illness diagnosis, ultimately aiding diabetic patients by maintaining their vision, by understanding the relative advantages of DE and GA in optimising neural network topologies.
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