| | --- |
| | license: mit |
| | language: |
| | - tr |
| | - en |
| | metrics: |
| | - accuracy |
| | - f1 |
| | - confusion_matrix |
| | base_model: |
| | - microsoft/resnet-50 |
| | pipeline_tag: image-classification |
| | tags: |
| | - biology |
| | --- |
| | Model Summary |
| | This model card describes two deep learning models trained to classify brain tumor MRI images into different tumor types. The models are based on ResNet50 and MobileNetV2 architectures and were trained using the Brain Tumor MRI Dataset available on Kaggle. They aim to assist medical professionals in detecting brain tumors using transfer learning approaches. |
| | Model Details |
| | ResNet50 |
| |
|
| | - Developed by: [Your Name or Organization] |
| | - Model type: Convolutional Neural Network (CNN) |
| | - Language(s): N/A (Image classification task) |
| | - License: [Specify license, e.g., MIT, Apache 2.0] |
| | - Finetuned from model: ResNet50 (ImageNet pretrained) |
| |
|
| | MobileNetV2 |
| |
|
| | - Developed by: [Your Name or Organization] |
| | - Model type: Convolutional Neural Network (CNN) |
| | - Language(s): N/A (Image classification task) |
| | - License: [Specify license, e.g., MIT, Apache 2.0] |
| | - Finetuned from model: MobileNetV2 (ImageNet pretrained) |
| |
|
| | Uses |
| | Direct Use |
| | These models can be used to classify MRI scans for brain tumor detection in clinical decision-support systems. |
| | Out-of-Scope Use |
| | Not intended for standalone diagnostic purposes without medical supervision. Misuse includes deployment without validation or interpretability assessments. |
| | Bias, Risks, and Limitations |
| | The model performance may vary depending on the image quality, scanner differences, and patient demographics. Models may inherit biases from the training data. |
| | Training Details |
| | Training Data |
| | The models were trained on the Brain Tumor MRI Dataset from Kaggle. The dataset contains images categorized into three classes: glioma, meningioma, and pituitary tumors. |
| | Training Hyperparameters |
| |
|
| | - Learning Rate: 0.0001 |
| | - Epochs: 25 |
| | - Batch Size: 32 |
| | - Optimizer: Adam |
| | - Loss Function: Categorical Cross-Entropy |
| |
|
| | Evaluation |
| | Testing Data |
| | Test data is a stratified split of the original dataset with unseen examples from each tumor class. |
| | Metrics |
| | Accuracy, Precision, Recall, F1-score |
| | Results |
| |
|
| | - ResNet50 Accuracy: ~98% |
| | - MobileNetV2 Accuracy: ~96% |
| |
|
| | Environmental Impact |
| |
|
| | - Hardware Type: NVIDIA GPU (e.g., RTX 3060 or Colab T4) |
| | - Hours Used: ~1-2 hours per model |
| | - Cloud Provider: Google Colab |
| | - Compute Region: [Not specified] |
| | - Carbon Emitted: Estimated using ML Impact calculator (minimal due to short training duration) |
| |
|
| | Technical Specifications |
| | Model Architecture and Objective |
| | Transfer learning using pretrained CNNs (ResNet50 and MobileNetV2) adapted for multi-class classification of brain tumor MRI images. |
| | Software |
| | Python 3.10, TensorFlow/Keras, NumPy, Matplotlib, OpenCV |
| |
|
