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afidzulinn/
brain-tumor-cnn

Name: brain-tumor-cnn
Availability: InStock

image-classification

pytorch

brain-tumor-detection

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Brain Tumor CNN Model

This model is a Convolutional Neural Network (CNN) trained to classify brain MRI images into two classes: "yes" (tumor present) and "no" (no tumor). It was trained on the Brain MRI Images for Brain Tumor Detection dataset from Kaggle.

Model Description

This model is a custom CNN built from scratch using PyTorch. It consists of two convolutional layers followed by max-pooling layers and two fully connected layers. The model was trained for 10 epochs with a learning rate of 0.001 using the Adam optimizer.

Intended Uses & Limitations

Intended Use: This model is intended for educational and research purposes to classify brain MRI images into tumor or no tumor.
Limitations: The model may not generalize well to MRI images from different sources or with different preprocessing.

Training and Evaluation Data

Training Data: 70% of the dataset (approximately 2100 images).
Validation Data: 15% of the dataset (approximately 450 images).
Test Data: 15% of the dataset (approximately 450 images).

Training Procedure

Training Hyperparameters

Learning Rate: 0.001
Batch Size: 32
Epochs: 10
Optimizer: Adam
Loss Function: CrossEntropyLoss

Framework Versions

PyTorch: 2.0.1
Transformers: 4.33.1
Datasets: 2.14.5

afidzulinn/brain-tumor-cnn