Evaluating the Effectiveness of Bat Algorithm in Optimizing Deep Learning Models for Parkinson's Disease Classification
Keywords:
Parkinson Disease, classification, Bat Algorithm, Early diagnosis, Deep learningAbstract
For early diagnosis and treatment, a precise classification of Parkinson's disease (PD) from medical imaging data is essential. Deep Learning (DL) models have showed promise in automating this procedure, but due to their intricate architectures and large parameter spaces, optimising these models is still difficult. In this study, the performance of DL models for PD classification using medical pictures is evaluated in relation to the Bat Algorithm (BA), a bio-inspired optimisation technique.The Bat Algorithm, which takes its cues from the echolocation technique used by bats, is renowned for its speedy exploration of challenging, non-convex search regions. We utilise BA to enhance the hyperparameters and topologies of DL models in order to increase the classification accuracy of these models for the diagnosis of PD. Our strategy uses BA to fine-tune model parameters in order to address DL frequent problems like overfitting.We conducted significant research on a dataset made up of MRI pictures of people with and without Parkinson's disease. The outcomes show how the Bat Algorithm may be used to optimise DL models for better classification performance. By quickly navigating the parameter space, BA helps to build models that more accurately generalise to new data and lower the danger of overfitting.To further demonstrate the benefits of our strategy, we contrast the performance of DL models optimised using standard methods and models optimised using BA. Area under the receiver operating characteristic curve (AUC-ROC) and other performance parameters including accuracy, sensitivity, and specificity are included in the evaluation.
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