This project uses the InceptionResNetV2 architecture, a powerful deep learning model pre-trained on ImageNet, for detecting brain tumors from MRI images.
he model is fine-tuned on a dataset of tumor and non-tumor images, achieving high accuracy. The project also includes visualization of the model's performance using various metrics and plots
This project aims to detect brain tumors in MRI images using a convolutional neural network (CNN) based on the InceptionResNetV2 architecture. The dataset includes images of brain MRIs categorized into "tumor" and "no tumor" classes.
base_modeltf.keras .applications.InceptionResNetV2(weights='imagenet', Incclude_top =False)>
We use the InceptionResNetV2 model pre-trained on the ImageNet dataset and modify it to suit our binary classification problem.This process helps in building an efficient brain tumor detection system using deep learning techniques.
We prepare the data generators for training, validation, and testing datasets and train the model.using article. We evaluate the model's performance on the test set and visualize the results using confusion matrices and accuracy scores.
To prevent overfitting and improve the model's generalization ability, we apply data augmentation techniques.ransfer learning leverages pre-trained models on large datasets (like ImageNet) and fine-tunes them for the specific task, which in this case is brain tumor detection. We use the InceptionResNetV2 model due to its proven efficiency in image classification tasks.
# Visualizing a few predictions helps in qualitatively understanding
# the model’s performance on individual images
prediction=model.predict( img_array)
predicted_class='Tumor' ifnp.argmax(prediction)==1else 'No Tumor'
# Display the image with actual and predicted labels
#To make the visulas more understandable
# Make prediction
axes[i].imshow(img)
axes[i].set_title(f"Actual: No Tumor\nPredicted: {predicted_class}")
axes[i].axis('off')Evaluation. permalink
Performance Metrics We use various metrics to evaluate the model's performance, such as accuracy, precision, recall, F1 score, and the confusion matrix.
#Get the information about the model performance
from sklearn.metrics import confusion_matrix, accuracy_score, classification_report
cm= confusion_matrix(y_true,y_pred_classes)
sns. heatmap(cm,annot=True,fmt='d',xticklabels=['No Tumor', 'Tumor'], yticklabels=['No Tumor', 'Tumor'], cbar=False)
plt. xlable('predicted')
plt .ylable('True')
plt. show()
plt. legend()
plt. tight_layout()
};
And that's the lot!
This detailed breakdown includes steps for data augmentation, leveraging transfer learning with InceptionResNetV2, compiling and training the model with early stopping, evaluating performance using multiple metrics, and visualizing the results.
This project demonstrates a comprehensive approach to building an effective brain tumor detection system using deep learning.
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