Transfer learning & fine-tuning for brain tumor detection from brain MRI Images
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Fine-tuning a binary image classification model for detecting brain tumor.
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The dataset consists of 253 brain MRI images.
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Each example is a 256 x 256 x 3 RGB image.
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The InceptionV3 architecture is used as base CNN model for transfer learning.
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Prefetching and multithreaded loading & preprocessing are implemented to improve speed.
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A dropout layer and data augmentation are implemented for strong regularization.
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The last 40 layers of the base model, InceptionV3, are unfreezed for fine-tuning.
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Accuracy of 92% is achieved on validation and test datasets.
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Dataset Source: https://www.kaggle.com/datasets/navoneel/brain-mri-images-for-brain-tumor-detection