Ppt for Alzheimer's disease detection and diagnosis

1. Contents
• Introduction
• Objective
• Literature Review
• Problem Statement
• Proposed Model
• Implementation
• Results
• Conclusion
• References
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2. • Alzheimer is a progressive neurological disorder that affects brain cells,impacting memory loss, thinking and
behavioral changes.
• Affects millions worldwide, with a growing prevalence due to an aging population.
• Early and accurate diagnosis remains a significant hurdle for effective treatment and management.
• To enhance early and accurate detection of Alzheimer's Disease using advanced technological solutions like
Federated Learning.
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Introduction

3. • Create a precise model for early Alzheimer's detection and diagnosis.
• Use Federated Learning for collaborative data analysis.
• Ensure privacy across diverse healthcare datasets.
• Improve accuracy while preserving sensitive information.
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Objective

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Literature Review
Table 1 : Literature Review
Ref.No Publisher/
Year of
Publication
Technologies Used Objective Strong Point Weak Point
1. Nature/ 2020 Federated learning,
IoMT
Design a smart
healthcare system
using Federated
Learning and
Internet of Medical
Things
• Privacy-Preserving
Decentralized Learning.
• Enhanced Personalized
Healthcare Insights.
• Device Heterogeneity
Complexity.
• Security Risks in
Connectivity.
2. Springer/2022 Federated learning,
blockchain
Using blockchain
with federated
learning to enhance
data privacy, security
and trust by ensuring
decentralized control
of data.
• Improved Security
Through Decentralization.
• Blockchain Trust for
Transparency
• Complex Integration
and Implementation.
• Potential Scalability
Challenges.

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Table 1 : Literature Review
Ref.No Publisher/
Year of
Publication
Technologies Used Objective Strong Point Weak Point
3. IEEE/ 2022 Federated learning,
Homomorphic
encryption and
Internet of Things
Using Homomorphic
encryption based
privacy preserving
federated learning in
IOT-enabled
healthcare system.
• Robust Privacy Protection
Mechanism.
• Secure Federated Learning
Framework.
• Homomorphic
Encryption Complexity.
• Limited Processing
Power Constraint.
4. IEEE/ 2022 Federated learning,
blockchain and
IoMT
Building a Federated
learning based
privacy preservation,
fraud detection and
blockchain IoMT
system for
healthcare.
• Personalized insights
without compromising
privacy.
• Ensuring integrity and
transparent transactions.
• Data flow challenges
affecting performance.
• High resource
demands during
deployment.

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Table 1 : Literature Review
Ref.No Publisher/
Year of
Publication
Technologies Used Objective Strong Point Weak Point
5. IEEE/2021 Federated learning Federated Learning
via Conditional
Mutual Learning for
Alzheimer’s Disease
Classification on
T1w MRI
• High structural detail for
brain analysis
• Established imaging
technique in Alzheimer's
diagnosis.
• Communication
Overhead
• Data Heterogeneity
6. MDPI/ 2023 Federated learning A Federated
Learning Model
Based on Hardware
Acceleration for the
Early Detection of
Alzheimer’s Disease
•Accelerated computation .
•Scalable.
• Dependency on
specific hardware
configurations

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Problem Statement
 "Developing an efficient and privacy-preserving method using federated learning for the accurate detection
and diagnosis of Alzheimer's disease, overcoming data fragmentation and privacy concerns in healthcare
datasets."

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Proposed Model
Fig 1: Model Diagram

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Federated Learning Workflow Diagram
Fig 2: Federated Learning Workflow Diagram

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The workflow of Federated Learning involves the following steps-
1. Initialization: Central server initializes a global model for federated learning.
2. Participant Selection: Edge devices, like smartphones or IoT devices, chosen for participation.
3. Model Distribution: Global model distributed to selected edge devices for local training.
4. Local Training: Edge devices train locally, using the global model as a starting point.
5. Model Update: Edge devices generate updates (gradients, weights) after local training.
6. Model Aggregation: Central server collects and aggregates updates to update the global model.
7. Iteration: Repeat steps 4-6 for multiple rounds to refine the global model iteratively.
8. Model Evaluation: Periodically assess the global model's accuracy and performance.
9. Deployment: Deploy the global model for inference while retaining it on edge devices.
10. Continuous Learning: System operates in a loop, periodically retraining the global model with the latest
data for continual adaptation to new patterns.

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Implementation
Dataset
 We have used a dataset from Kaggle called “Alzheimer MRI Dataset” in our project.
 The Dataset consists of MRI (Magnetic Resonance Imaging) Images.
 All the images are resized into 128 x 128 pixels.
 The Dataset consists of 6400 MRI images.
 The Dataset has four classes of images-
 Class - 1: Mild Demented (896 images)
 Class - 2: Moderate Demented (64 images)
 Class - 3: Non Demented (3200 images)
 Class - 4: Very Mild Demented (2240 images)

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Implementation (Cont.…)
Fig 3: Mild Demented Brain MRI Samples
Fig 4: Moderate Demented Brain MRI Samples

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Implementation (Cont.…)
Fig 5: Non Demented Brain MRI Samples
Fig 6: Very Mild Demented Brain MRI Samples

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Implementation (Cont.…)
Fig 8 : Loss and Accuracy

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Result
Fig 7: Comparing Predicted Classes with the Actual Classes from the Test Data

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Conclusion
• Federated Learning can revolutionize early Alzheimer's detection while ensuring data privacy.
• With its collaborative data analysis approach, it safeguards personal information.
• This method has the potential to transform how we diagnose and manage Alzheimer's.
• Ultimately, it may lead to improved treatments and better care strategies for those affected.

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References
1. Nicola Rieke, Jonny Hancox, Wenqi Li, Fausto Milletarì, Holger R. Roth, Shadi Albarqouni, Spyridon
Bakas, Mathieu N. Galtier, Bennett A. Landman, Klaus Maier-Hein, Sébastien Ourselin, Micah Sheller,
Ronald M. Summers, Andrew Trask, Daguang Xu, Maximilian Baust & M. Jorge Cardoso. The future of
digital health with federated learning. Nature/ 2020.
2. Attia Qammar, Ahmad Karim, Huansheng Ning & Jianguo Ding. Securing federated learning with
blockchain: a systematic literature review. Springer/2022.
3. Li Zhang, Jianbo Xu, Pandi Vijayakumar, Pradip Kumar Sharma, Uttam Ghosh. Homomorphic Encryption-
Based Privacy-Preserving Federated Learning in IoT-Enabled Healthcare System. IEEE/ 2022.
4. Abdullah Lakhan, Mazin Abed Mohammed, Jan Nedoma, Radek Martinek. Federated-Learning Based
Privacy Preservation and Fraud-Enabled Blockchain IoMT System for Healthcare. IEEE/ 2022.
5. Ya-Lin Huang; Hao-Chun Yang; Chi-Chun Lee. Federated Learning via Conditional Mutual Learning for
Alzheimer’s Disease Classification on T1w MRI. IEEE/ 2021.
6. Kasem Khalil ,Mohammad Mahbubur Rahman Khan Mamun , Ahmed Sherif, Mohamed Said, Ahmad
Abdel-Aliem Imam. MDPI/ 2023.

18. Thank You
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