A density-based MS disease diagnosis model using the capuchin search algorithm and an ensemble of deep neural networks

• Published in: Scientific reports Vol. 14; no. 1; pp. 31721 - 17
• Main Authors: Bai, LiJuan, Wu, Jiao, Chen, Li, Jiang, Xin, Song, ZhuYin
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 30.12.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166; 639/705; Algorithms; Brain - diagnostic imaging; Brain - pathology; Capuchin search algorithm; Deep ensemble learning; Diagnosis; Humanities and Social Sciences; Humans; Image processing; Image Processing, Computer-Assisted - methods; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical diagnosis; multidisciplinary; Multiple sclerosis; Multiple Sclerosis - diagnosis; Multiple Sclerosis - diagnostic imaging; Multiple Sclerosis - pathology; Neural networks; Neural Networks, Computer; Neuroimaging; Optimization techniques; Principal Component Analysis; Principal components analysis; Science; Science (multidisciplinary); Topology; Capuchin search algorithm; Multiple sclerosis; Magnetic resonance imaging; Deep ensemble learning
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-82395-7

Multiple sclerosis (MS) is a severe brain disease that permanently destroys brain cells, impacting vision, balance, muscle control, and daily activity. This research employs a weighted combination of deep neural networks and optimization techniques for MS disease diagnosis. This method uses slices of magnetic resonance imaging (MRI) images as input. Then, after the pre-processing operation, the process of segmentation and identification of the region of interest (ROI) is performed using a combination of the fuzzy c-means (FCM) algorithm and the capuchin search algorithm (CapSA) algorithm. When the target view is detected, the features of each ROI are extracted through three techniques: local binary pattern (LBP), multi-linear principal component analysis (MPCA), and gray level co-occurrence matrix (GLCM). Each of these features is then processed by a deep neural network. In each deep neural network, the CapSA algorithm is used to determine the optimal topology structure and adjust the weight vector of the neural network. This means that in this process, the vector and topology of the deep neural network are adjusted using the CapSA algorithm in such a way that the training error is minimized. Finally, after creating the trained models, the weighted combination of the outputs of these three models is used for the final diagnosis. The implementation results showed that our method was successful in achieving 100% precision compared to other comparative methods. Also, in the average accuracy criterion, it showed a performance of 99.51%, which shows the high performance of our method in diagnosing patients.