PARKINSON'S DISEASE CLASSIFICATION BASED ON STACKED DENOISING AUTOENCODER

One of the most common neurological conditions caused by gradual brain degeneration is Parkinson's disease (PD). Although this neurological condition has no known treatment, early detection and therapy can help patients improve their quality of life. An essential patient's health record is made of medical images used to control, manage, and treat diseases. However, in computerbased diagnostics, disease classification is a difficult task because of the time consumption 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 augmentation is 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 for an 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.