- Fine-tuning an image classification model on a Colorectal-Cancer-Histology dataset.
- This is a biological 8-class classification problem.
- The dataset consists of 5000 images.
- Each example is a 150 x 150 x 3 RGB image of one of 8 classes.
- The class labels are: ("tumor", "stroma", "complex", "lympho", "debris", "mucosa", "adipose", "empty")
- The state-of-the-art CNN, ResNet50V2, is used as base model.
- The last 10 layers of the base model are unfreezed for fine-tuning.
- Data augmentation is implemented for regularization.
- Learning rate reduction callback is implemented.
- F1-score and confusion matrix are visualized.
- Accuracy of 94% is achieved on validation and test datasets.
- Dataset Source: https://www.tensorflow.org/datasets/catalog/colorectal_histology
- Dataset homepage: https://zenodo.org/record/53169#.XGZemKwzbmG
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