# Improving Alzheimer’s Disease and Brain Tumor Detection Using Deep Learning with Particle Swarm Optimization

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## Abstract

**:**

## 1. Introduction

- We develop a hybrid framework that employs the PSO algorithm to determine the best hyper-parameters’ configuration for CNN architectures to improve prediction accuracy for brain diseases and decrease the loss function value.
- We utilize PSO as a wrapper around the training process to retrieve hyper-parameters (such as the number of convolution filters, the size of the filters used in the convolutional layer, the size of the pool in the max pooling layer, and the size of the strides used in the max pooling layer).
- We contrast our PSO-optimized CNN model with three distinct CNN models: the ResNet, the InceptionNet, and the VGG models. Finally, we benchmarked our proposed model against state-of-the-art models employing different optimization algorithms.

## 2. Related Work

Reference | Dataset Type | Proposed Model | Study Limitation | Evaluation Results |
---|---|---|---|---|

[18] | AD Dataset | 3D CNN (VoxCNN, ResNet), Softmax | Small dataset size, model complexity, lack of interpretability and external validation | AD vs. NC Accuracy: 79% VoxCNN Accuracy: 80% ResNet AUC: 88% VoxCNN AUC: 87% ResNet |

[19] | AD Dataset | CNN, SVM | Lack of implementation, training, and parameter details | AD vs. NC Accuracy: 96% |

[20] | AD Dataset | CNN (AlexNet), SVM | Insufficient discussion on feature selection and extraction from MRI data | AD vs. NC Accuracy: 90% Specificity: 91% Sensitivity: 87% |

[21] | AD Dataset | FreeSurfer, SVM, Naive Bayesian, Random Forest, Decision Tree | Limitations in choice of evaluation metrics | AD vs. NC Accuracy: 75% Specificity: 77% Sensitivity: 75% F-score: 72% AUC: 76% |

[22] | AD Dataset | PSO-based PIDC algorithm, Softmax | Lack of extensive details and evaluation of the hybrid algorithm for brain image segmentation | AD vs. NC Accuracy: 92% |

[23] | AD Dataset | PSO with Decision Tree Methods | Insufficient analysis or discussion of feature selection process | AD vs. NC Accuracy: 93.56% |

[24] | AD Dataset | GA algorithm, ELM, PSO | Lack of thorough comparison with other classifiers or methods | AD vs. NC Accuracy: 87.23% |

[25] | Brain Tumor Dataset | CNN, Softmax | Insufficient details or analysis of CNN architecture for brain tumor classification | Normal vs. Not Normal Accuracy: 94.39% |

[26] | Brain Tumor Dataset | GA algorithm, SVM | Lack of extensive details or analysis of optimization technique for brain tumor detection | Normal vs. Not Normal Accuracy: 91% |

[27] | Brain Tumor Dataset | CNN, ELM, SVM | Insufficient details or analysis of ensemble classifier for brain tumor segmentation and classification | Normal vs. Not Normal Accuracy: 91.17% |

[29] | Brain Tumor Dataset | CNN (VGG19), Softmax | Lack of details or analysis of deep CNN architecture and hyper-parameters for brain tumor classification | Normal vs. Not Normal Accuracy: 90.67% |

[31] | Brain Tumor Dataset | CNN with DWT, SVM | Lack of details or analysis of PSO-based segmentation technique for brain MRI images and comparison with other methods | Normal vs. Not Normal Accuracy: 85% |

[32] | Brain Tumor Dataset | CNN with PSO, Softmax | Lack of details or analysis of modified PSO algorithm for brain tumor detection and limitations of the modification | Normal vs. Not Normal Accuracy: 92% |

Reference | Usage of PSO |
---|---|

[18] | Not used PSO |

[19] | Not Used PSO |

[20] | Not Used PSO |

[21] | Not Used PSO |

[22] | PSO was used for optimizing the model performance by selecting the optimal parameters and weight |

[23] | PSO was used for the feature selection process |

[24] | PSO was used for optimizing the model performance by selecting the optimal parameters and weight |

[25] | Not used PSO |

[26] | Not used PSO |

[27] | Not used PSO |

[29] | Not used PSO |

[31] | PSO was used for the feature selection process |

[32] | PSO was used for the feature selection process |

## 3. Proposed Methodology

#### 3.1. MRI Datasets

#### 3.2. Data Pre-Processing

#### 3.3. Proposed Detection Framework

#### 3.3.1. Convolutional Neural Network (CNN)

#### 3.3.2. Particle Swarm Optimization (PSO)

Algorithm 1: Particle Swarm Optimization (PSO) Algorithm | |||||||

Require: Objective function $g:{\mathbb{R}}^{n}\to \mathbb{R}$ | |||||||

Require: Hyperparameters n, Fitness function F | |||||||

Ensure: Optimal solution ${\lambda}^{*}$ | |||||||

#### 3.3.3. Optimal Selection of Hyper-Parameters via PSO Algorithm

- The number of filters in convolutional layers.
- The size of filters in convolutional layers.
- The size of the pool in the max pooling layer.
- The size of the strides used in the max pooling layer.

- 1.
**Initialization of the Swarm**In the swarm, each particle’s initial position ${\lambda}_{i}$ in the n-dimensional space is randomly selected from a uniform distribution U(${b}_{l}$, ${b}_{u}$), where ${b}_{l}$ and ${b}_{u}$ represent the lower and upper limits. The particle’s position ${\lambda}_{i}$ is then designated as its best-known position, denoted by ${\lambda}_{i}$. If the fitness value $g\left({\lambda}_{i}\right)$ exceeds the fitness value of the swarm’s best global position, $g\left({\lambda}^{S}\right)$, ${\lambda}_{i}$ is stored as the new best position in the swarm, referred to as ${\lambda}^{S}$. The particle’s velocity ${v}_{i}$ is randomly determined from a uniform distribution, considering the constraints of the hyper-parameter limits. Following the initialization, the swarm, consisting of s particles represented as tuples $({\lambda}_{i},{v}_{i},{\lambda}_{i}={\lambda}_{i})$ for $i=0,1,\dots ,s$, undergoes evolutionary processes.- 2.
**Evaluation of the Swarm**In each generation of a swarm (referred to as gen, where ${G}_{\mathrm{max}}$ represents the maximum number of generations), the velocity values of all particles are updated using the following equation:$${v}_{i}\leftarrow \omega {v}_{i}+{\varphi}_{prp}({\lambda}_{i}^{*}-{\lambda}_{i})+{\varphi}_{grg}({\lambda}^{S}-{\lambda}_{i})$$Here, $rp$ and $rg$ are randomly drawn from a uniform distribution $U(0,1)$ to add a stochastic element to the velocity updates, enhancing search space exploration. The inertia weight $\omega $ scales the velocity, while ${\varphi}_{p}$ and ${\varphi}_{g}$ are acceleration coefficients that determine the influence of the best particle position (${\lambda}_{i}^{*}$) and the best swarm position (${\lambda}^{S}$) on the velocity changes. Subsequently, the particle’s position ${\lambda}_{i}$ is updated accordingly.Following this, the best position for each particle and the best swarm position are modified. These updates are only applied if there has been a change. The evolutionary process continues until one of the following termination conditions is met:- (a)
- The best position in the swarm (${\lambda}^{S}$) has been displaced by an amount smaller than a specified minimum step size denoted as $\delta $.
- (b)
- The fitness value of the best particle has improved by an amount less than a predefined threshold denoted by $\u03f5$.
- (c)
- The maximum number of swarm generations, ${G}_{\mathrm{max}}$, has been reached.

The first termination condition is designed to prevent high-quality oscillation between two neighboring solutions. The second condition is satisfied when the swarm optimization converges to a well-fitted particle unlikely to improve further. Finally, the best position in the swarm (${\lambda}^{S}$) is returned as the output.The efficiency of PSO is influenced by the number of hyper-parameters involved, and this can be denoted as$${T}_{PSO}=s\xb7g\left({\lambda}_{k}\right)\xb7{G}_{\mathrm{max}}$$Figure 4 illustrates the whole architecture of our methodology used in this study. The CNN initially uses the PSO algorithm for parameter optimization. The PSO is initialized in this process by the execution parameters, and this generates the particles. Each solution represents a completed CNN training period because each particle is a possible solution, and its position has a parameter that needs to be used in the proposed CNN architecture. Our CNN architecture is designed with a concise yet flexible structure. It comprises a block comprising convolutional and max pooling layers, followed by a Softmax activation function for classification. Table 3 and Table 4 list the convolutional and maximum pooling layer parameters and the permitted ranges for each.

- 1.
**Input database for the CNN training**: this step chooses the database that will be processed and classified for CNN. It is important to note that each database’s components must maintain a consistent structure or set of attributes with the same pixel size and file format.- 2.
**Produce the particle population needed by the PSO algorithm**: the PSO parameters were set to include the experiment’s number of iterations, particle numbers, inertial weight, cognitive constant (C1), and social constant (C2). Table 4 lists the PSO parameters used in the experiment.- 3.
**Set up the CNN architecture**: create the CNN architecture using the PSO parameter (the number of filters and the size of the filters in the convolution layers, the size of the pool in the max pooling layer, and the size of the strides in the max pooling layer), along with the additional parameters listed in Table 4.- 4.
**Validation and training for CNN**: after reading and processing the input databases and collecting the images for training, validation, and testing, the CNN generates a recognition rate in this step. The objective function’s return value includes these values for the PSO.- 5.
**Determine the objective function**: the PSO algorithm evaluates the objective function defined in Equation (1) to select the best parameters.- 6.
**Update the PSO parameters**: each particle adjusts its velocity and location at each iteration based on its best position (Pbest) in the search space and the best position for the entire swarm (Gbest).- 7.
**Repeat the process**: the number of iterations is the stopping criterion in our study, which involves evaluating all the particles until the stopping criteria are satisfied.- 8.
**Select the optimal solution**: the particle Gbest represents the best solution in this process for the CNN model.

## 4. Experiments and Results

#### 4.1. Optimization Results Obtained by the PSO-CNN Method

#### 4.2. Comparison with Existing Transfer Learning Model

#### 4.3. Comparison with Existing Transfer Learning Model

## 5. Conclusions and Future Work

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

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**Figure 9.**Alzheimer’s disease classification: (

**a**) incorrect image classification (

**b**) correct image classification.

Layer Type | Used Parameters | Parameter Value |
---|---|---|

Convolutional Layer (C) | Filter Size (${s}_{F}\times {s}_{F}$) | ${s}_{F}\ge 2$ |

Number of Filters (n) | $n\ge 1$ | |

Max Pooling Layer (P) | Stride/Step Size (ł) | $\u0142\ge 2$ |

Size of the Max Pooling Layer (${s}_{P}$) | ${s}_{P}\ge 2$ |

Parameters of CNN | |
---|---|

Learning Function | Adam |

Activation Function | Softmax |

Non-linearity Activation Function | ReLU |

Epochs | 20 |

Batch Size | 32 |

Parameters of PSO | |

Particles | 4 |

Iterations | 14 |

Inertial weight (W) | 0.5 |

Social constant (W2) | 0.5 |

Cognitive constant (W1) | 0.5 |

Hyper-Parameters $[\mathit{n},\mathit{sf},\mathit{sp},\mathit{l}]$ | Accuracy (%) |
---|---|

$[8,7,4,3]$ | 97.72 |

$[7,6,3,3]$ | 96.96 |

$[8,7,2,4]$ | 95.87 |

$[14,8,4,3]$ | 95.79 |

$[13,8,4,3]$ | 91.97 |

$[9,8,2,4]$ | 97.43 |

$[8,7,4,3]$ | 97.00 |

$[1,5,2,3]$ | 84.20 |

$[1,5,2,2]$ | 50.00 |

$[12,8,4,3]$ | 98.50 |

Best Values of $[\mathit{n},\mathit{sf},\mathit{sp},\mathit{l}]$ = [12, 8, 4, 3] |

Hyper-Parameters $[\mathit{n},\mathit{sf},\mathit{sp},\mathit{l}]$ | Accuracy (%) |
---|---|

$[2,8,2,4]$ | 91.88 |

$[16,2,4,4]$ | 97.34 |

$[5,4,3,4]$ | 95.47 |

$[16,7,2,4]$ | 98.67 |

$[16,5,4,4]$ | 98.44 |

$[5,7,3,4]$ | 93.90 |

$[13,6,4,4]$ | 97.89 |

$[11,6,4,4]$ | 94.22 |

$[13,7,2,4]$ | 96.80 |

$[8,6,4,4]$ | 95.94 |

$[1,5,2,2]$ | 85.40 |

$[15,7,2,4]$ | 98.83 |

Best Values of $[\mathit{n},\mathit{sf},\mathit{sp},\mathit{l}]$ = [15, 7, 2, 4] |

Hyper-Parameters $[\mathit{n},\mathit{sf},\mathit{sp},\mathit{l}]$ | Accuracy (%) |
---|---|

$[8,5,3,4]$ | 96.80 |

$[10,4,2,2]$ | 95.52 |

$[12,7,4,2]$ | 96.48 |

$[15,6,3,2]$ | 96.64 |

$[11,6,2,2]$ | 95.68 |

$[1,5,2,2]$ | 82.40 |

$[5,5,2,2]$ | 60.53 |

$[16,6,3,4]$ | 95.36 |

$[13,6,3,4]$ | 94.88 |

$[12,6,3,4]$ | 93.76 |

$[12,5,3,2]$ | 97.12 |

Best Values of $[\mathit{n},\mathit{sf},\mathit{sp},\mathit{l}]$ = [12, 5, 3, 2] |

Dataset | Metrics (%) | ||||
---|---|---|---|---|---|

Accuracy | Precision | Recall | AUC | False Negative Rate (FNR) | |

ADNI Dataset | 98.50 | 97.53 | 98.60 | 99.83 | 1.72 |

AD Dataset | 98.83 | 98.15 | 99.22 | 99.88 | 1.56 |

Brain Tumor Dataset | 97.12 | 92.66 | 99.02 | 99.24 | 1.98 |

Transfer Learning Model | Dataset | Metrics (%) | |||
---|---|---|---|---|---|

Accuracy | Precision | Recall | AUC | ||

VGG16 | ADNI Dataset | 82 | 51.08 | 50.03 | 50.78 |

AD Dataset | 82 | 50.61 | 62.66 | 50.61 | |

Brain Tumor Dataset | 94 | 66.34 | 65.56 | 50.74 | |

Inception V3 | ADNI Dataset | 50.50 | 49.50 | 50.26 | 48.07 |

AD Dataset | 50.05 | 50.03 | 50.35 | 47.05 | |

Brain Tumor Dataset | 90.03 | 68.49 | 68.06 | 49.54 | |

ResNet50 | ADNI Dataset | 68 | 49.07 | 50.38 | 49.27 |

AD Dataset | 69 | 51.03 | 51.19 | 51.40 | |

Brain Tumor Dataset | 87 | 55.59 | 53.97 | 50.63 |

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## Share and Cite

**MDPI and ACS Style**

Ibrahim, R.; Ghnemat, R.; Abu Al-Haija, Q.
Improving Alzheimer’s Disease and Brain Tumor Detection Using Deep Learning with Particle Swarm Optimization. *AI* **2023**, *4*, 551-573.
https://doi.org/10.3390/ai4030030

**AMA Style**

Ibrahim R, Ghnemat R, Abu Al-Haija Q.
Improving Alzheimer’s Disease and Brain Tumor Detection Using Deep Learning with Particle Swarm Optimization. *AI*. 2023; 4(3):551-573.
https://doi.org/10.3390/ai4030030

**Chicago/Turabian Style**

Ibrahim, Rahmeh, Rawan Ghnemat, and Qasem Abu Al-Haija.
2023. "Improving Alzheimer’s Disease and Brain Tumor Detection Using Deep Learning with Particle Swarm Optimization" *AI* 4, no. 3: 551-573.
https://doi.org/10.3390/ai4030030