![235309204-68caaa9a-f47b-4b2e-8f53-aca277099d80](https://private-user-images.githubusercontent.com/103449830/304396132-d2cbfe7e-4746-4d7e-893a-0a6badf8e837.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3MTU0MDc4MDUsIm5iZiI6MTcxNTQwNzUwNSwicGF0aCI6Ii8xMDM0NDk4MzAvMzA0Mzk2MTMyLWQyY2JmZTdlLTQ3NDYtNGQ3ZS04OTNhLTBhNmJhZGY4ZTgzNy5wbmc_WC1BbXotQWxnb3JpdGhtPUFXUzQtSE1BQy1TSEEyNTYmWC1BbXotQ3JlZGVudGlhbD1BS0lBVkNPRFlMU0E1M1BRSzRaQSUyRjIwMjQwNTExJTJGdXMtZWFzdC0xJTJGczMlMkZhd3M0X3JlcXVlc3QmWC1BbXotRGF0ZT0yMDI0MDUxMVQwNjA1MDVaJlgtQW16LUV4cGlyZXM9MzAwJlgtQW16LVNpZ25hdHVyZT01MGViYzlkYzlmZDJhZTZiNjU5ODg2MDdkZTNlZWNlNjYwYzY0ZGI3MjI3YzZkMWM4MGU3MjU1NTg4NDE1NDcxJlgtQW16LVNpZ25lZEhlYWRlcnM9aG9zdCZhY3Rvcl9pZD0wJmtleV9pZD0wJnJlcG9faWQ9MCJ9.SZlTcDw1myCjqbVl3dqRAr4-zVaMgdZ5ICZQM3_L4ao)
https://www.kaggle.com/datasets/navoneel/brain-mri-images-for-brain-tumor-detection
The dataset has 253 samples, which are divided into two classes with tumor and non-tumor. The number of people with brain tumor is 155 and people with non-tumor is 98.
- The optimizer is set to Adam.
- The loss function is set to binary cross-entropy, which is used for binary classification problems.
- The evaluation criterion is set to accuracy, which is used to measure the performance of the model during training and testing.
- The batch_size parameter specifies the number of samples per gradient update.
- The epochs parameter specifies the number of iterations in the entire training dataset.
- The validation_data parameter specifies the validation data used during training.
- The model is trained for 22 epochs, which means it is repeated 22 times on the entire training dataset.
- During training, the performance of the model is evaluated based on the validation data. This helps prevent overfitting and ensures that the model generalizes well to new data.
Accuracy | Loss |
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