# Omdena - Early Detection and Diagnosis of Alzheimer's Disease through Brain Scan Analysis

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Background

Brain-related disorders, such as Alzheimer’s disease, Parkinson’s disease, and Multiple Sclerosis, are a growing global concern. As per the World Health Organization, neurological disorders are responsible for 9% of all deaths globally, and Alzheimer’s and other dementias alone are among the top ten leading causes of death worldwide.

With a rapidly aging population, these numbers are expected to rise significantly over the coming years. Despite significant advances in medical technology, early detection and accurate diagnosis of these conditions remain challenging. Traditionally, the diagnosis of these disorders has been based on clinical assessments and symptoms. However, these methods are often subjective and may not detect the disease until it has significantly progressed.

Introduction

Alzheimer's disease is a complex neurodegenerative disorder that affects millions of people worldwide. Early detection and prediction of Alzheimer's can lead to better management and treatment outcomes. This prediction system utilizes a machine learning model trained on a dataset of relevant features to provide predictions about the likelihood of Alzheimer's disease.

About Alzheimer's Disease

Alzheimer's disease (AD) is a progressive neurodegenerative disease. Though best known for its role in declining memory function, symptoms also include: difficulty thinking and reasoning, making judgements and decisions, and planning and performing familiar tasks. It may also cause alterations in personality and behavior. The cause of AD is not well understood. There is thought to be a significant hereditary component. For example, a variation of the APOE gene, APOE e4, increases risk of Alzheimer's disease.

Problem

The goal of this project is to leverage the power of artificial intelligence, specifically machine learning and computer vision techniques, to analyze brain scan images for the early detection and diagnosis of Alzheimer’s disease, Parkinson’s disease, and Multiple Sclerosis.

Our aim is to create an AI model that can analyze these images, identify patterns that may be indicative of these disorders, and make predictions with high accuracy. The expectation is that such a tool could supplement existing diagnostic practices, providing a more objective and potentially earlier indication of these diseases. We believe that an accurate and efficient AI diagnostic tool can significantly improve the prognosis and quality of life for millions of patients globally.

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Demo

​ Once the project is deployed, put the demo link here. ​

1. If the project is not deployed that you can skip this section ​ <Add documentation link here, if any> ​ <Add graphics as needed. Example: dataset sample, project’s title image etc.> ​

Contribution Guidelines

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• Have a Look at the project structure and folder overview below to understand where to store/upload your contribution
• If you're creating a task, Go to the task folder and create a new folder with the below naming convention and add a README.md with task details and goals to help other contributors understand
  • Task Folder Naming Convention : task-n-taskname.(n is the task number) ex: task-1-data-analysis, task-2-model-deployment etc.
  • Create a README.md with a table containing information table about all contributions for the task.
• If you're contributing for a task, please make sure to store in relavant location and update the README.md information table with your contribution details.
• Make sure your File names(jupyter notebooks, python files, data sheet file names etc) has proper naming to help others in easily identifing them.
• Please restrict yourself from creating unnessesary folders other than in 'tasks' folder (as above mentioned naming convention) to avoid confusion. ​

Project Structure

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├── LICENSE
├── README.md          <- The top-level README for developers/collaborators using this project.
├── original           <- Original Source Code of the challenge hosted by omdena. Can be used as a reference code for the current project goal.
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│
├── reports            <- Folder containing the final reports/results of this project
│   └── README.md      <- Details about final reports and analysis
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│
├── src                <- Source code folder for this project
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    ├── data           <- Datasets used and collected for this project
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    ├── docs           <- Folder for Task documentations, Meeting Presentations and task Workflow Documents and Diagrams.
    │
    ├── references     <- Data dictionaries, manuals, and all other explanatory references used
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    ├── tasks          <- Master folder for all individual task folders
    │
    ├── visualizations <- Code and Visualization dashboards generated for the project
    │
    └── results        <- Folder to store Final analysis and modelling results and code.
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Folder Overview

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• Original - Folder Containing old/completed Omdena challenge code.
• Reports - Folder to store all Final Reports of this project
• Data - Folder to Store all the data collected and used for this project
• Docs - Folder for Task documentations, Meeting Presentations and task Workflow Documents and Diagrams.
• References - Folder to store any referneced code/research papers and other useful documents used for this project
• Tasks - Master folder for all tasks
  • All Task Folder names should follow specific naming convention
  • All Task folder names should be in chronologial order (from 1 to n)
  • All Task folders should have a README.md file with task Details and task goals along with an info table containing all code/notebook files with their links and information
  • Update the task-table whenever a task is created and explain the purpose and goals of the task to others.
• Visualization - Folder to store dashboards, analysis and visualization reports
• Results - Folder to store final analysis modelling results for the project. ​

Project Setup

​ ​ Open the Command line or Terminal ​

• Clone the repository ​

git clone https://dagshub.com/Omdena/TorontoCanadaChapter_BrainScanImages.git
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• Move to the folder ​

cd <folder name>
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• To open with VSCode ​

code .
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• To open with jupyter notebook (or maually open using jupyter notebook app) ​

jupyter notebook
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