This project focuses on the development of a Deep Neural Network (DNN) capable of detecting brain tumors from MRI scans. Brain tumors can be challenging to detect in their early stages, often leading to various symptoms such as memory loss, hallucinations, and impaired motor function as they progress. To address this issue, our team has constructed a robust DNN solution that can assist in the early detection of tumors.
We have utilized a dataset containing over 3000 MRI images, classified into four folders, to train and test our model.
Architecture Diagrams: We provide visual representations of our model architectures, preprocessing steps, and deployment strategies.
Classification Model: The final trained DNN model is made available in .h5 or .pkl format.
Notebooks: We have included all the notebooks used for data preprocessing and experimentation as .ipynb files. The final notebook with our best-performing model is labeled as "TeamName_FinalNotebook.ipynb."
Our goal is to contribute to the field of medical imaging by providing an effective tool for early tumor detection through MRI scans.