README.md

metadata

license: apache-2.0
datasets: brain-tumor-image-dataset-semantic-segmentation
metrics:
  - accuracy
  - f1
  - precision
  - recall
pipeline_tag: image-classification
tags:
  - brain-tumor
  - image-classification
  - keras
  - tensorflow
  - cnn
  - mri
  - healthcare

Tumor Detection ML Model

Model Description

This model is designed to classify brain tumor images using a Convolutional Neural Network (CNN). It has been trained and fine-tuned on a labeled dataset of brain tumor MRI images.

Training Details

• Framework: TensorFlow/Keras
• Optimizer: Adam with a learning rate scheduler
• Loss Function: Categorical Crossentropy
• Data Augmentation: Includes rotation, width/height shift, zoom, and horizontal flipping.
• Hyperparameter Tuning: Performed using Keras Tuner.

Metrics

The following metrics were used to evaluate the model's performance:

• Accuracy: Measures the overall correctness of predictions.
• F1 Score: Balances precision and recall.
• Precision: Indicates the proportion of true positives among positive predictions.
• Recall: Indicates the proportion of true positives among all actual positives.

Usage

You can load the model using the Hugging Face Transformers library:

from transformers import AutoModel
model = AutoModel.from_pretrained("YourUsername/Tumor_detection_ML_Model")