MRI analysis for volume estimation of the hippocampus using machine learning for early Alzheimer\'s disease diagnosis

Alzheimer?s disease is a brain disorder that slowly destroys memory and thinking skills and, eventually, the ability to carry out the simplest tasks. One common thing that is found among all the Alzheimer's patients is that parts of their brain began to shrink in size because proteins plaques form o

Project Title

MRI analysis for volume estimation of the hippocampus using machine learning for early Alzheimer\'s disease diagnosis

Project Area of Specialization

Artificial Intelligence

Project Summary

Alzheimer’s disease is a brain disorder that slowly destroys memory and thinking skills and, eventually, the ability to carry out the simplest tasks. One common thing that is found among all the Alzheimer's patients is that parts of their brain began to shrink in size because proteins plaques form on the brain cells and the neurons fail to function normally, which disrupts the work of brain cells (neurons) and triggers a series of toxic events. Neurons are damaged, lose connections to each other and eventually die. Through Magnetic Resonance Imaging (MRI), Medical science has enabled us to photograph the brain from all angles. Although this technology is great for diagnosis it is difficult for even experienced doctors to take a look at MRI and diagnose diseases. Through the implementation of computer-based diagnosis techniques and Content-Based Image Retrieval (CBIR), we are going to create an AI model to help predict Alzheimer’s disease using MRI. To implement the diagnosis techniques, we are going to use 3D-Capsule Network and Joint Label Fusion for early detection of Alzheimer's. A 3D-Capsule Networks (CapsNets) is capable of fast learning, even for small datasets, and can effectively handle robust image rotations and transitions while the joint label fusion allows separating the parts of the brain namely the hippocampus that is used for diagnosis of the disease.

Project Objectives

• To develop an AI model that is able to classify the stages of Alzheimer's disease to help doctors identify the disease early and stop its progression.  
• To identify how AI can be implemented to provide more ways to diagnose neurological diseases.  
• To use AI to empower the current medical equipment to perform more efficiently and have effective results.

Project Implementation Method

The project will be implanted using various python libraries to implement the Capsule network algorithm, preprocess data, segmentation, and testing of the results.

The first step will be obtaining the MRI dataset then it will be preprocessed and specific regions of the brain will be separated from the whole brain image with their volumetric data. After that, the data will be used to train the Capsule net algorithm that will be able to detect Alzheimer's Disease at its early stages.

Benefits of the Project

• The project shall eliminate the need for a long and deep study of MRIs, for the sake of diagnosis of the Disease.
• Further, it will help detect the severity of the disease and help in having suitable medicines to lessen or prolong the effect of the disease. With suitable medication for early detection, it can prolong the disease to have its effects on the patient.
• This project will pave the way to detecting other neurodegenerative diseases and may help in curing them.

Technical Details of Final Deliverable

• The Final deliverables will contain the research document containing the process and the results of the project with the trained AI model that can detect Alzheimer’s Disease at its early stages.

Final Deliverable of the Project

Software System

Core Industry

Medical

Other Industries

Core Technology

Artificial Intelligence(AI)

Other Technologies

Sustainable Development Goals

Good Health and Well-Being for People, Industry, Innovation and Infrastructure

Required Resources

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