The "Brain Tumor Computer Vision" project aimed to develop an accurate and efficient computer vision solution for detecting brain tumors in medical images. By leveraging the power of EfficientNetB2, a state-of-the-art convolutional neural network architecture, along with transfer learning techniques, this project provided accurate diagnoses and assisted medical professionals in identifying potential brain tumors.
- Implemented a deep learning model for brain tumor detection using the EfficientNetB2 architecture.
- Utilized transfer learning to leverage pre-trained weights and adapt the model to medical image data.
- Trained the model on a well-curated dataset of brain MRI scans, achieving high accuracy.
- Developed an intuitive user interface for uploading MRI scans and obtaining predictions.
- Deployed the trained model using Hugging Face's space for seamless sharing and accessibility.
- EfficientNetB2 Model: EfficientNetB2 which is known for its efficiency and effectiveness in image classification tasks was used for this project.
- Transfer Learning: Transfer learning accelerated model training by starting from pre-trained weights, allowing the model to learn specific features relevant to brain tumor detection without extensive data.
- Dataset: A carefully curated dataset of brain MRI scans which was gotten from kaggle was used to train and validate the model in order to ensure its ability to generalize to real-world cases.
- User Interface: The project featured an intuitive user interface that enabled users to upload MRI scans, submit them for analysis, and receive predictions.
- Deployment: Hugging Face's space was used for deploying the trained model, making it accessible for medical professionals and researchers.
- Python: The primary programming language for model development and interface creation.
- PyTorch: Utilized for building and training the EfficientNetB2-based model.
- Transfer Learning: Leveraged for adapting the pre-trained EfficientNetB2 to brain MRI data.
- Hugging Face Space: Used for deploying and sharing the trained model.
The project was deployed here
- The project laid the groundwork for further enhancements, such as multi-class classification for different tumor types, interpretability of model decisions, and integration with medical systems.
- A user-friendly interface will be developed using a suitable framework (e.g., Flask, Django, or a web-based GUI library).
For inquiries or more information, please contact [Onabajo Monsurat] at [[email protected]].