Project Outline:
Deep Neural Networks (DNNs) can be applied to Parkinson Disease diagnosis detections. DNNs are a type of artificial intelligence (AI) system that is capable of learning from large amounts of data. DNNs can be used to diagnose Parkinson disease by analyzing patterns in medical data such as genetic, demographic, clinical, and imaging data.
In this work, we classified meanders and spirals images drawn by the control group and PD patients using a CNN-based approach. . The Dataset include Healthy and Parksion images, that are taken by people that includes handwritten images like lines., then the DNN will be trained on the imaging data to detect any abnormalities associated with Parkinson disease.
Once the DNN is trained, it can then be used to make predictions about whether or not a patient has Parkinson disease.
Application
- Help diagnose Parkinson Disease in real-world.
- This can help doctors make more informed decisions about the patient’s care.
- Can be used in clinical trials to help quickly and accurately detect Parkinson Disease in potential study participants
- Can be used in research to help identify new treatments or therapies for the condition.
Hardware and software requirements:
Hardware Requirements:
- Processor – intel i3generation
- RAM – 4 GB (min)
- Hard Disk – 20 GB
- Key Board – Standard Windows Keyboard
- Mouse – Two or Three Button Mouse
- Monitor – SVGA
Software Requirements:
- Operating System – Windows 10
- Technology – Python
- Front End – Tkinter
- IDLE – Python 3.7 or higher
What You’ll Learn after doing this project?
- You will have a better understanding of how to use DNNs to analyze different types of data in order to make predictions.
- How to buid an AI model for training the dataset and make the prediction for input given.
- Create a UI to display the result and performance graph.
