This project focuses on classifying brain tumor images using deep learning techniques. It utilizes MRI images to accurately classify tumors into different categories: glioma tumor, meningioma tumor, pituitary tumor, or no tumor. The model is built using Convolutional Neural Networks (CNNs) and trained on a dataset containing MRI images of various brain tumors.
The dataset consists of MRI images categorized into four classes: glioma tumor, meningioma tumor, pituitary tumor, and images with no tumor. The images are preprocessed and resized to ensure compatibility with the CNN model.
The CNN model architecture comprises several convolutional layers followed by max-pooling layers and dropout layers to prevent overfitting. The final layer utilizes softmax activation to classify the input image into one of the four tumor categories.
The model is trained using the Adam optimizer with categorical cross-entropy loss. Training is conducted over multiple epochs, with validation performed on a separate subset of the dataset to monitor model performance and prevent overfitting.
The trained model's performance is evaluated using accuracy metrics on both the training and validation sets. Additionally, loss curves are plotted to visualize the model's convergence during training.
The trained model can be deployed to predict tumor categories for new MRI images. The deployment process involves loading the saved model and using it to classify new images.
The model can predict the tumor category for new MRI images. Users can input an MRI image, and the model will output the predicted tumor category.
Abdul Mannan
Note: The model's performance can vary based on the quality and diversity of the dataset used for training. Further improvements and fine-tuning may be necessary for optimal performance in real-world applications.