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

被引：0
作者：
Y. Mohana Roopa
B. Bhaskar Reddy
Meenigi Ramesh Babu
R. Krishna Nayak
机构：
[1] Institute of Aeronautical Engineering,Computer Science and Engineering
[2] St. Peter’s Engineering College,Electronics and Communication Engineering
[3] Cognizant Technology Solutions India Pvt. Ltd,AIA Department
[4] Vignan Institute of Technology and Management for Women,Computer Science and Engineering
来源：
Multimedia Tools and Applications | 2023年 / 82卷
关键词：
Alzheimer’s disease; Segmentation; Deep CNN; Optimization; Features;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
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页码：33333 / 33356
页数：23
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共 102 条
  • [1] Aderghal K(2020)Improving Alzheimer's stage categorization with Convolutional Neural Network using transfer learning and different magnetic resonance imaging modalities Heliyon 6 e05652-53
  • [2] Afdel K(2020)Detection of Alzheimer’s disease and dementia states based on deep learning from MRI images: a comprehensive review J Instit Electron Comput 1 39-13
  • [3] Benois-Pineau J(2020)Deep ensemble learning for Alzheimer's disease classification J Biomed Inform 105 103411-304
  • [4] Catheline G(2018)Medical image analysis using convolutional neural networks: a review J Med Syst 42 1-387
  • [5] Altinkaya E(2019)Computer aided Alzheimer's disease diagnosis by an unsupervised deep learning technology Neurocomputing 392 296-185
  • [6] Polat K(2020)Automatic assessment of Alzheimer’s disease diagnosis based on deep learning techniques Comput Biol Med 120 103764-149
  • [7] Barakli B(2019)Alzheimer's disease facts and figures Alzheimers Dement 15 321-39719
  • [8] An N(2018)DenseNet for anatomical brain segmentation Procedia Comput Sci 140 179-159
  • [9] Ding H(2009)Spatially augmented LPboosting for AD classification with evaluations on the ADNI dataset Neuroimage 48 138-113
  • [10] Yang J(2021)Improving the prediction of heart failure patients’ survival using SMOTE and effective data mining techniques IEEE Access 9 39707-295
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