Amyotrophic Lateral Sclerosis (ALS) is a neurological illness that progresses unabated and currently lacks an effective disease-modifying treatment. In this research, the prediction of Amyotrophic Lateral Sclerosis using Bidirectional Three-Dimensional Quasi-Recurrent Neu-ral Network (PALS-Bi-3DQRNN) is proposed. Initially, the input image is gathered from the Canadian ALS Neuroimaging Consortium (CALSNIC) dataset. Then, the image is provided for the pre-processing phase. During the pre-processing phase, the Unsharp Mask Guided Filtering (UMGF) is used for motion correction. The pre-processed images are provided to the feature extraction process; the Spatial-Frequency Transformer (SFT) is used to extract local and global discriminative features, such as colour distribution, spatial layout, edges, and textures. Although Bi-3DQRNN can learn discriminative representations via gradient-based training, its performance depends strongly on the selection of hyperparameters. Therefore, the Waterwheel Plant Optimization Algorithm (WPOA) is employed to optimally tune the Bi-3DQRNN parameters, leading to improved classification of ALS and healthy subjects. The proposed method is carried out using Python. Several performance metrics are used to assess the effectiveness of the proposed PALS-Bi-3DQRNN method, including Sen-sitivity, Precision, Accuracy, F1-score, Specificity, and Receiver Operating Characteristic (ROC). The proposed PALS-Bi-3DQRNN method attains 22.36%, 15.42% and 18.27% higher sensitivity, 19.36%, 26.42% and 23.27% higher accuracy, 28.27%, 19.36% and 23.42% higher F1-score contrasted with existing techniques, Examples include the identifi-cation of manifold learning for amyotrophic lateral sclerosis functional loss assessment (ML-ALS-FLA), amyotrophic lateral sclerosis from multi-centre magnetic resonance imag-ing data using a spatial and frequency fusion transformer (ALS-MCMD-SFFT), and the comparison of King’s clinical staging in multinational amyotrophic lateral sclerosis cohorts (CKCS-MALSC).
Ramalakshmanna et al. (Thu,) studied this question.