Deep Learning Model Training and Evaluation Framework for Diabetic Retinopathy Detection Using PSO-Optimized Hyper parameters
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Currently, numerous healthcare physicians are having hitches detecting diabetic retinopathy early in patients. This disease's primary warning indications are challenging to detect. A clear-cut instruction technique is compulsory to detect this condition timely. Deep learning is one method to use for this purpose. This work used a particle swarm optimization (PSO) technique to select the optimal Diabetic Retinopathy. Applying deep learning with particle swarm optimization (PSO) resulted in a 73.11% increase in outcome. This VGG19 model exhibits training and validation losses of 0.755 and 0.7151, respectively. VGG19 shows the finest simplification capability, as realized since the minor variance among training and validation victims. This model realizes steadily on together training and hidden data.
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References
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