Teaching learning-based brain storm optimization tuned Deep-CNN for Alzheimer’s disease classification

• Published in: Multimedia tools and applications Vol. 82; no. 21; pp. 33333 - 33356
• Main Authors: Roopa, Y. Mohana, Reddy, B. Bhaskar, Babu, Meenigi Ramesh, Nayak, R. Krishna
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2023; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Alzheimer's disease; Artificial neural networks; Brain research; Classification; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Deep learning; Diagnosis; Engineering; Game theory; Image segmentation; Machine learning; Magnetic resonance imaging; Multimedia; Multimedia Information Systems; Neural networks; Optimization; Optimization algorithms; Performance evaluation; Special Purpose and Application-Based Systems; Support vector machines; Track 2: Medical Applications of Multimedia; Features; Alzheimer’s disease; Segmentation; Optimization; Deep CNN
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-023-14815-1

Alzheimer’s disease (AD) as an incurable disease that affects the mental functions of the patients. The early diagnosis of AD requires automatic Computer-Aided Diagnosis, which needs advanced technical practices like Deep Learning algorithms. This paper proposes a hybrid optimization tuned Deep convolutional neural network (Deep CNN) based automatic AD detection strategy. The significance of the proposed strategy lies in the optimal segmentation of Magnetic resonance imaging (MRI) and the classification of AD using the Deep CNN, which is trained using the proposed teaching learning-based brainstorm (TLBS) optimization algorithm that inherits the characteristic features of learning search agents and the associative search agents to obtain the global optimal solution for tuning the weights of the classifier. The performance evaluation using the accuracy, sensitivity, specificity, and F-Measure are obtained 97.03%, 97.18%, 97.03%, and 97.39% respectively, which shows the effectiveness of the proposed method in detecting the AD of patients.