Efficient feature fusion model withmodified bidirectional LSTM for automatic Parkinson's disease classification
Enhanced sunflower optimisation; Gait recognition; GAIT-IT; GAIT-ITS; Improved convolutional neural network; Median filter; Modified bidirectional LSTM; Parkinsons disease
The majority of people affected by Parkinsons disease (PD) are middle-aged and older. The condition causes a variety of severe symptoms, including tremors, limited flexibility, and slow movements. As Parkinsons disease develops with changing symptoms and growing severity, the importance of computer-aided diagnosis based on algorithms cannot be highlighted. Gait recognition technology appears to be a potential path for Parkinson's disease identification since it captures unique properties of a persons walking pattern without requiring active participation, providing stability and non-intrusiveness. To begin,the median filter is used to remove noise from the input images received during data collection. This paper describes a new method for finding local and global features in gait images to assess the severity of Parkinsons disease.Local features are extracted using a stacked autoencoder, and global features are obtained using an Improved Convolutional Neural Network (ICNN). The Enhanced Sunflower Optimisation (ESO) technique is used to improve the CNN model's performance by optimizing hyperparameters such as batch size, learning rate, and number of convolutional layers. To classify PD severity, a modified bidirectional LSTM (MBi-LSTM) classifier receives input in the form of a combination of local and global features. The proposed model's performance is completely evaluated with the GAIT-IT and GAIT-IST datasets, which include key measures such as accuracy, precision, recall, and the F-measure. This study improves the diagnosis of Parkinsons disease by introducing a non-intrusive real-time monitoring system capable of early detection and prevention. Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.
Reshma S.; Chennakesavulu M.; Patil S.S.; Lamani M.R.
International Journal of Information Technology (Singapore), Vol-16, No. 6, pp. 3963-3971.
Springer Science and Business Media B.V.
Reshma S., Dept of Artificial Intelligence and Machine Learning, Dayananda Sagar College of Engineering, Bangalore, India; Chennakesavulu M., Department of Electronics and Communication Engineering, Rajeev Gandhi Memorial College of Engineering and Technology (Autonomous), Andhra Pradesh, Nandyal, 518501, India; Patil S.S., Rajarambapu Institute of Technology, Rajaramnagar, Uran Islampur, India; Lamani M.R., CHRIST(Deemed to Be University), Kanmanike, Kumbalgudu, Mysore Road, Karnataka, Bangalore, 560074, India