Detection of Alzheimer’s Disease Based on Cloud-Based Deep Learning Paradigm
Abstract
:1. Introduction
1.1. Motivation
1.2. Contribution
2. Materials and Methods
Dataset
Alzheimer Stages | AD | LMCI | NC | EMCI |
---|---|---|---|---|
Subject number | 75 | 75 | 75 | 75 |
Male/female | 21/54 | 43/32 | 32/43 | 51/24 |
Age (mean ± STD) | 7 5.95 ± 0.91 | 77.44 ± 1.33801 | 75.68 ± 0.469617 | 76.08 ± 0.89684 |
Algorithm 1: Proposed Model |
Step 1: Input the Dicom image from MRI scans |
Step 2: Pre- Process the images and converting them to jpeg format and removing noice |
Step 3: Reformat the images and resize them from 256 × 256 to 224 × 224 |
Step 4: Images are classified into EMCI, NC, LMCI and AD. |
Step 5: GoogleNet Model method uses transfer learning technique for training 268 pre trained images and classify input images as AD and Normal case |
Step 6: A web based application is designed to assist docters to check AD from remote place using local application and Microsoft Azure plaform |
3. Results and Discussion
- (i)
- It leverages the pre-trained weights obtained from training millions of images in a database.
- (ii)
- It reduces the time required for training a learning model.
- (iii)
- It helps minimize errors in generalization.
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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References | Modality | Data Processing/Training | Classifier | AD: NC acc. | SEN | SPE | cMCI:nMCI aa. | SEN | SPE | AD | cMCI | ncMCI | NC | Total |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Li et al. (2014) [19] | MRI, PET | 3D CNN | Logistic regression | 92.97 | 76.21 | 198 | 167 | 236 | 229 | 830 | ||||
Suk et al. (2014) [20] | MRI, PET | DBM | SVM | 97.35 | 94.6 | 95.2 | 75.92 86.75 (MCI:NC) | 48.04 95.37 | 95.23 65.87 | 93 | 76 | 128 | 101 | 398 |
Li et al. (2015) [21] | MRI, PET, CSF | RBM + drop out | SVM | 91.4 | 57.4 76.21 (MCI:NC) | 51 | 43 | 56 | 52 | 202 | ||||
Suk et al. (2015) [22] | MRI, PET, CSF | SAE + sparse learning | SVM | 98.8 | 83.3 90.7 (MCI:NC) | 51 | 43 | 56 | 52 | 202 | ||||
Liu et al. (2015) [23] | MRI, PET | SAE with zero masking | Softmax | 91.4 | 92.32 | 90.42 | 92.1 | |||||||
Suk and Shen (2013) [24] | MRI, PET, CSF | SAE | SVM | 95.9 | 75.8 | 51 | 43 | 56 | 52 | 202 | ||||
Cheng and Liu (2017) [25] | MRI, PET | 3D CNN+ 2D CNN | Softmax | 89.64 | 87.10 | 92.00 | 199 | 229 | 428 | |||||
Vu et al. (2017) [26] | MRI, PET | SAE+ 3d CNN | Softmax | 91.14 | 145 | 172 | 317 | |||||||
Liu et al. (2014) [27] | MRI, PET | SAE+ NN | Softmax | 87.76 | 88.57 | 87.22 | 76.92 | 74.29 | 78.13 | 65 | 67 | 102 | 77 | 311 |
Choi and ** (2018) [28] | MRI, PET | 3D CNN | Softmax | 96 | 93.5 | 97.8 | 84.2 | 81.0 | 87.0 | 139 | 79 | 92 | 182 | 494 |
Lu et al. (2018) [29] | PET | DNN + NN | Softmax | 84.6 | 80.2 | 91.8 | 82.93 | 79.69 | 83.84 | 238 | 217 | 409 | 360 | 1224 |
Model: “Sequential” | ||
---|---|---|
Layer (Type) | Output Shape | Param |
GoogleNet (functional) | (1,64,24,24) | 20,024,184 |
Flatten (Flatten) | (1,4608) | 0 |
dense (Dense) | (1,192,12,12) | 4,710,612 |
dense_1 (Dense) | (1,480,6,6) | 524,800 |
dense_2 (Dense) | (1,832,3,3) | 131,528 |
dense_3 (Dense) | (1,1024,1,1) | 32,996 |
dense_4 (Dense) | (1,10) | 8 |
Trainable params: 25,424,128 | ||
Non-trainable params: 0 |
Approach | Dataset | Modality | Classification Type | Accuracy |
---|---|---|---|---|
Hosseini-Asl et al. [50] | 210 subjects (70 AD, 70CAD-dementia NC, 70 MCI) ADNI | MRI/CT | Binary/Multi | AD vs. EMC vs. HC: 89.1% AD + MCI/NC: 90.3% AD/NC: 97.6% MCI/NC: 90.8% |
Sahumbaiev et al. [51] | 530 subjects (185 AD, 185, MCI, 160 HC) ADNI | MRI/CT | Multi | AD/MCI/NC: 88.31% |
Korolev et al. [52] | 50 AD, 43 LMCI, 77 EMCI, 61 NC- ADNI | MRI/CT | Binary | AD vs. NC: 80% AD vs. EMCI: 63% AD vs. LMCI: 59% LMCI vs. NC: 61% LMCI vs. EMCI: 52% EMCI vs. NC: 56% |
Juan Ruiz et al. [53] | 600 brain MRI images- ADNI | MRI/CT | Multi | AD, EMCI, LMCI, NC: 66.67% |
Proposed fine-tuned GoogleNet model | 300 subjects (75 AD, 75 EMCI, 75 LMCI, 75 NC) | MRI/CT | Multi | AD/EMCI/LMCI/NC: 98% |
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Pruthviraja, D.; Nagaraju, S.C.; Mudligiriyappa, N.; Raisinghani, M.S.; Khan, S.B.; Alkhaldi, N.A.; Malibari, A.A. Detection of Alzheimer’s Disease Based on Cloud-Based Deep Learning Paradigm. Diagnostics 2023, 13, 2687. https://doi.org/10.3390/diagnostics13162687
Pruthviraja D, Nagaraju SC, Mudligiriyappa N, Raisinghani MS, Khan SB, Alkhaldi NA, Malibari AA. Detection of Alzheimer’s Disease Based on Cloud-Based Deep Learning Paradigm. Diagnostics. 2023; 13(16):2687. https://doi.org/10.3390/diagnostics13162687
Chicago/Turabian StylePruthviraja, Dayananda, Sowmyarani C. Nagaraju, Niranjanamurthy Mudligiriyappa, Mahesh S. Raisinghani, Surbhi Bhatia Khan, Nora A. Alkhaldi, and Areej A. Malibari. 2023. "Detection of Alzheimer’s Disease Based on Cloud-Based Deep Learning Paradigm" Diagnostics 13, no. 16: 2687. https://doi.org/10.3390/diagnostics13162687