Team Leader Email - dhiraajkv14112002@gmail.com
Our project is designed to improve the speed and accuracy of processing an Medical image and detect the Brain Tumour using a Deep Learning model. Our system fetch the input from the user and analyse the image using the Deep Learning model that is trained using Intel DevCloud for best processing. The result of the analysis will detect the Brain Tumour and highlight the same for easy viewing and analysis of the user. The users can able to download the analysed file and thus shortens the time taken to analyse and report. This provides the best solution for the existing problem with the help of Intel One API.
In today’s scenario, Medical image processing is a time consuming and extensive task. Technologies like DC-Net algorithm and DC-Net++ algorithm has an accuracy of 93.04% and 95.03% respectively. Till now a Neuropathologist has to examine the MRI images to determine a tumour type and grade. This takes around 5-7 days.
Our project is built in such a way that the time taken by image processing is reduced by incorporating SYLC Intel API that enables parallel computing. Also using a Deep- Learning model, enables the computer to identify the tumour from the source image provided. Our project is built with a user-friendly interface for easy accessing of tools and along with the incorporation of Intel API and fast processing algorithm helps in replacing the existing technology and stands out to solve the problem.
- Intel SYCL/C++ Library
- Intel Distribution for Python
- Tensor Flow AI kit
- Intel VTune Profiler 2023.1
- Intel Advisor 2023.1
- Jupyter Notebook
- Visual Studio Code
- Python 3.11
- Anaconda Navigator
- Streamlit
- Intel DevCloud Platform
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Write about the biggest learning you had while developing the prototype