Parkinson’s disease classificationbased onstacked denoising autoencoder

One of the most common neurological conditions caused by gradual brain degen-eration is Parkinson’s disease (PD). Although this neurological condition hasno known treatment, early detection and therapy can help patients improvetheir quality of life. An essential patient’s health record is made of medicalimages used to control, manage, and treat diseases. However, in computer-based diagnostics, disease classification is a difficult task because of the timeconsumption and high rate of false positive marks. To overcome this problem, this paper introduces a stacked denoising autoencoder (SDA) for Parkinson’s disease classification. In preprocessing, noise is reduced and important information is retained, resulting in increased performance and data augmentationis performed to avoid overfitting issues and increase the size of the dataset.The main aim of this paper is to derive an optimal feature selection design foran effective Parkinson’s disease classification. Improved Pigeon-Inspired Optimization (IPIO) algorithm is introduced to enhance the performance of the classifier. Thus, the classification result improved by the optimal features and also increased the sensitivity, accuracy, and specificity in the medical image diagnosis. The proposed scheme is implemented in PYTHON and compared with traditional feature selection models and other classification approaches. The efficacy of the performances is evaluated using a Parkinson’s Progression Markers Initiative (PPMI) dataset. The integration of the stacked denoising autoencoder and Improved pigeon inspired optimization method produced the greatest results, with 99.17% accuracy, 98.74% sensitivity, and 98.96% specificity. Furthermore, our finding outperforms the most recent research in the field.