The Brain Tumor Detection System is a computer vision model developed to detect the presence of brain tumors in MRI scans. This project aims to assist medical professionals in the early detection and diagnosis of brain tumors, potentially improving patient outcomes.
The model architecture used for brain tumor detection is a Convolutional Neural Network (CNN). The CNN architecture consists of several convolutional and pooling layers followed by fully connected layers.
The model was trained using a dataset of MRI scans containing images with and without brain tumors. Data preprocessing techniques such as data augmentation were applied to increase the diversity of the training dataset and improve model generalization. The model was trained using the Adam optimizer with binary crossentropy loss.
The performance of the model was evaluated using metrics such as accuracy, precision, recall, and F1-score. These metrics were calculated on both the training and validation datasets to assess the model's performance and generalization ability.
- Dataset: Brain MRI Images for Brain Tumor Detection
- Pretrained Models: Keras Applications
- John Doe
- Jane Smith
We would like to thank the creators of the Brain MRI Images for Brain Tumor Detection dataset for making their data publicly available.