Alzheimer’s Disease Prediction Using Deep Feature Extraction and Optimization
Abstract
:1. Introduction
- Transfer learning is employed on VGG19 with fine-tuned hyperparameters for deep feature extraction from the fc7 and fc8 layers.
- The process of feature concatenation is performed to create a unified feature space by considering the highest value.
- Redundancy in the features is eliminated using an updated version of the WoA with optimal settings.
2. Related Work
3. Materials and Methods
3.1. Dataset
3.2. Deep Feature Extraction
3.3. Feature Concatenation
Algorithm 1: Pseudo code of feature concatenation |
Normalize () Normalize () if length () < length (): Extend to match the length of else: Extend to match the length of = empty_vector of length () used_features = empty_set for i from 0 to length () − 1: weighted__value = * [i] weighted__value = * [i] if weighted__value > weighted__value and [i] no longer in used_features: [i] = [i] else if weighted_Y_2_value >= weighted__value and [i] no longer in used_features: [i] = [i] else: [i] = some_fallback_logic () used_features.Upload ([i]) |
3.4. Feature Optimization
Whale Optimization Algorithm
Algorithm 2: Pseudo code of the whale optimization algorithm |
Pseudocode of whale optimization algorithm Step 1: Initialization (Whale population) where Step 2: The computation of fitness for every solution. best search agent Step 3: While For every solution Updated and If1 If2 Revise the present location of the search agent by Equation (1). Else if2 The process of selecting a random search agent. The location of the search agent is subject to modification based on Equation (7). End if2 Else if1 The location of the search agent is subject to modification by Equation (5). End if1 End for Examine the trajectory of a search agent if it deviates from its designated search location and alters its course. Change if the better solution is presented End While Step 4: Return |
4. Results
4.1. AD Prediction Results of Features Extracted from fc7
4.2. AD Prediction Results of Features Extracted from fc8
4.3. AD Prediction Results of Feature Fusion Extracted from fc7 and fc8
4.4. AD Prediction Results of Feature Optimization
5. Discussion
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Ref. | Year | Dataset | Model | Accuracy |
---|---|---|---|---|
[6] | 2022 | MRI Brain Images | DL model | 96% |
[7] | 2022 | MRI | Deep TL model | 93.52% |
[10] | 2023 | MRI Images | DL-based radiomics | 93.50% |
[18] | 2022 | MRI, PET | AD dataset | 94.61% |
[19] | 2023 | MRI | Deep CNN | 89% |
[20] | 2023 | Brain Dataset | AlexNet framework | 98.35% |
[22] | 2023 | MRI | CNN | 98.67% |
[23] | 2022 | MRI | ML | 94.64% |
[24] | 2022 | MRI | DL and ML | 91.70% |
[25] | 2022 | MRI | ML | 95.30% |
[26] | 2022 | MRI | DL | 82.2% |
Method | Accuracy (%) | Precision (%) | Recall (%) | F1 Score (%) | FNR (%) | Time (s) |
---|---|---|---|---|---|---|
F-KNN | 97.3 | 95.4 | 96.8 | 96.1 | 2.7 | 900 |
C-SVM | 96.4 | 93.1 | 95.6 | 94.3 | 3.6 | 712 |
Q-SVM | 94.2 | 91.2 | 94.3 | 92.7 | 5.8 | 656 |
MG-SVM | 91.8 | 92 | 91.6 | 91.6 | 8.2 | 989 |
W-KNN | 92.3 | 92.3 | 92.6 | 92.3 | 7.7 | 1256 |
ES-KNN | 97.1 | 97.3 | 96.3 | 96.3 | 2.9 | 1522 |
ESD | 93.9 | 94 | 94 | 93.66 | 6.1 | 1823 |
Classes | Accuracy (%) | Precision (%) | Recall (%) | F1 Score (%) |
---|---|---|---|---|
AD | 98.31 | 98.31 | 98.31 | 98.31 |
CI | 97.71 | 97.71 | 97.71 | 97.71 |
CN | 98.56 | 98.56 | 98.56 | 98.56 |
Method | Accuracy (%) | Precision (%) | Recall (%) | F1 Score (%) | FNR (%) | Time (s) |
---|---|---|---|---|---|---|
F-KNN | 96.2 | 96 | 96 | 96.33 | 3.8 | 900 |
C-SVM | 95.5 | 95.6 | 95.4 | 95.6 | 4.5 | 712 |
Q-SVM | 91.7 | 91.6 | 92 | 92 | 8.3 | 809.35 |
MG-SVM | 88.5 | 88.3 | 88.3 | 88.6 | 11.5 | 840.25 |
W-KNN | 91.2 | 91.3 | 91.66 | 91 | 8.8 | 1182.7 |
ES-KNN | 95.5 | 95.6 | 95.6 | 95.6 | 4.5 | 2167.9 |
ESD | 88.4 | 88 | 88.6 | 88.33 | 11.6 | 18,446 |
Classes | Accuracy (%) | Precision (%) | Recall (%) | F1 Score (%) |
---|---|---|---|---|
AD | 98.09 | 98.09 | 98.09 | 98.09 |
CI | 96.48 | 96.48 | 96.48 | 96.48 |
CN | 97.88 | 97.88 | 97.88 | 97.88 |
Method | Accuracy (%) | Precision (%) | Recall (%) | F1 Score (%) | FNR (%) | Time (s) |
---|---|---|---|---|---|---|
F-KNN | 98 | 97 | 98.3 | 97.6 | 2 | 972 |
C-SVM | 98 | 97.6 | 98.3 | 98 | 2 | 912.2 |
Q-SVM | 96.4 | 96.3 | 96.3 | 96.3 | 3.6 | 879 |
MG-SVM | 94.4 | 94.3 | 94.6 | 94.3 | 5.6 | 1420.5 |
W-KNN | 92.4 | 92.3 | 92.6 | 92.3 | 7.6 | 1882.7 |
ES-KNN | 97.5 | 97.3 | 97.3 | 97.6 | 2.5 | 2667.9 |
ESD | 95.7 | 95.6 | 96 | 95.6 | 4.3 | 3752.2 |
Classes | Accuracy (%) | Precision (%) | Recall (%) | F1 Score (%) |
---|---|---|---|---|
AD | 98.64 | 98.64 | 98.64 | 98.64 |
CI | 98.31 | 98.31 | 98.31 | 98.31 |
CN | 98.98 | 98.98 | 98.98 | 98.98 |
Method | Accuracy (%) | Precision (%) | Recall (%) | F1 Score (%) | FNR (%) | Time (s) |
---|---|---|---|---|---|---|
F-KNN | 99 | 99.6 | 99.6 | 99.6 | 1 | 12 |
C-SVM | 97.5 | 98 | 98 | 98 | 2.5 | 39.15 |
Q-SVM | 95.3 | 95.3 | 95.3 | 95 | 4.7 | 54.42 |
MG-SVM | 94 | 94.3 | 94.3 | 94 | 6 | 61.52 |
W-KNN | 92 | 92 | 92.3 | 91.66 | 8 | 65.35 |
ES-KNN | 92.4 | 92.6 | 92.3 | 92.3 | 7.6 | 89.65 |
ESD | 97.3 | 97.3 | 97.3 | 97 | 2.7 | 109.35 |
Reference | Year | Method | Accuracy |
---|---|---|---|
[44] | 2018 | Deep CNN | 82.4% |
[45] | 2019 | ConvNets | 97.65% |
[46] | 2020 | Deep CNN | 91% |
[47] | 2021 | Deep CNN | 98.57% |
[7] | 2022 | Ensemble of ML and DL | 96% |
[12] | 2022 | Segmentation and DL | 93.50% |
[49] | 2022 | DL-based ontology | 94.61% |
[48] | 2023 | Deep CNN | 93.52% |
[28] | 2023 | DL-based Alzheimer Net | 98.67% |
Proposed | DL and Feature Optimization | 99% |
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Mohammad, F.; Al Ahmadi, S. Alzheimer’s Disease Prediction Using Deep Feature Extraction and Optimization. Mathematics 2023, 11, 3712. https://doi.org/10.3390/math11173712
Mohammad F, Al Ahmadi S. Alzheimer’s Disease Prediction Using Deep Feature Extraction and Optimization. Mathematics. 2023; 11(17):3712. https://doi.org/10.3390/math11173712
Chicago/Turabian StyleMohammad, Farah, and Saad Al Ahmadi. 2023. "Alzheimer’s Disease Prediction Using Deep Feature Extraction and Optimization" Mathematics 11, no. 17: 3712. https://doi.org/10.3390/math11173712
APA StyleMohammad, F., & Al Ahmadi, S. (2023). Alzheimer’s Disease Prediction Using Deep Feature Extraction and Optimization. Mathematics, 11(17), 3712. https://doi.org/10.3390/math11173712