Description: The objective of this project is to develop a machine learning model that can accurately detect brain tumors in medical imaging data, such as MRI scans. By leveraging advanced image processing techniques and machine learning algorithms, the model aims to assist healthcare professionals in the early and accurate diagnosis of brain tumors, which can lead to timely treatment interventions and improved patient outcomes.
Early and Accurate Diagnosis: The developed model can aid healthcare professionals in detecting brain tumors at an early stage, leading to timely intervention and improved patient outcomes.
Time and Cost Efficiency: Automated brain tumor detection can reduce the time and effort required for manual analysis of MRI scans, enabling healthcare professionals to focus on treatment planning and patient care.
Improved Detection Accuracy: By leveraging advanced machine learning algorithms, the model can potentially achieve higher accuracy rates in brain tumor detection compared to traditional manual interpretation of scans.
Assistive Tool for Healthcare Professionals: The model serves as an assistive tool for healthcare professionals, providing additional support and insights in the diagnosis process.
Research Advancement: The project contributes to the field of medical imaging and machine learning research by exploring novel techniques for brain tumor detection and advancing the understanding of brain tumor analysis using artificial intelligence.
# clone the repository from github
git clone https://github.com/deepraj21/Neuroscan# enter into the root directory
cd Neuroscan# make a python virtual Environment
python -m venv venv# activate the virtual environment
./venv/Scripts/activate# install the dependencies from the requirements.txt
pip install -r requirements.txt# Run the flask webapp
flask run
# OR
python app.py
