A Novel End-to-End Hybrid Network for Alzheimer’s Disease Detection Using 3D CNN and 3D CLSTM

Project Code :TMMAAI64


A novel unified CNN framework for AD identification, where both 3D CNN and 3D convolutional long short-term memory (3D CLSTM) are employed


In this work, we will detect the Alzheimer’s disease using deep learning techniques (U Net architecture). Alzheimer’s disease (AD) is an irreversible neurodegenerative disease. It is reported that there are 50 million people in the world suffering from dementia. 

Magnetic resonance imaging (MRI) plays an important role in Alzheimer’s disease (AD) detection as it shows morphological changes caused by brain atrophy. Convolutional neural network (CNN) has been successfully used to achieve good performance in accurate diagnosis of AD. 

The U-net is a convolutional network architecture for fast and precise segmentation of images mainly for medical images. Experiments will demonstrate that our model will provide best detection performance compared to the state-of-the-art methods.

Keywords: Alzheimer’s disease, Detection, Magnetic Resonance Images, Convolutional Neural Networks, U Net architecture.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram


Software: Matlab 2018a or above


Operating Systems:

  • Windows 10
  • Windows 7 Service Pack 1
  • Windows Server 2019
  • Windows Server 2016


Minimum: Any Intel or AMD x86-64 processor

Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support


Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation

Recommended: An SSD is recommended A full installation of all MathWorks products may take up to 29 GB of disk space


Minimum: 4 GB

Recommended: 8 GB

Learning Outcomes

  • Introduction to Matlab
  • How to start with MATLAB
  • About Matlab language
  • Matlab coding skills
  • About tools & libraries
  • Application Program Interface in Matlab
  • About Matlab desktop
  • How to use Matlab editor to create M-Files
  • Features of Matlab
  • Basics on Matlab
  • What is an Image/pixel?
  • About image formats
  • Introduction to Image Processing
  • How digital image is formed
  • Importing the image via image acquisition tools
  • Analyzing and manipulation of image.
  • Phases of image processing:
    • Acquisition
    • Image enhancement
    • Image restoration
    • Color image processing
    • Image compression
    • Morphological processing
    • Segmentation etc.,
  • About Artificial Intelligence (AI)
  • About Machine Learning
  • About Deep Learning
  • About layers in AI (input, hidden and output layers)
  • Building AI (ANN/CNN) architecture using Matlab
  • We will able to know, what’s the term “Training” means in Artificial Intelligence
  • About requirements that can influence the AI training process:
    • Data
    • Training data
    • Validation data 
    • Testing data 
    • Hardware requirements to train network
  • How to detect the disease using AI
  • How to extend our work to another real time applications
  • Project development Skills
    • Problem analyzing skills
    • Problem solving skills
    • Creativity and imaginary skills
    • Programming skills
    • Deployment
    • Testing skills
    • Debugging skills
    • Project presentation skills
    • Thesis writing skills

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