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BinarySandia04
2026-05-21 14:15:14 +02:00
parent d218003b45
commit 65e084556d
1 changed files with 10 additions and 7 deletions
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skin_cancer_classification.py
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@@ -23,14 +23,11 @@ Original file is located at
| mel | Melanoma | Malignant | | mel | Melanoma | Malignant |
| akiec | Actinic keratoses / Bowen's disease | Benign | | akiec | Actinic keratoses / Bowen's disease | Benign |
## 0. Requirements ## 1. Requirements and dataset download
""" """
# !pip install pandas numpy matplotlib seaborn pillow scikit-learn tensorflow import sys
IN_COLAB = 'google.colab' in sys.modules
"""## 1. Download dataset"""
# !pip install --upgrade kagglehub[pandas-datasets,hf-datasets]
import kagglehub import kagglehub
@@ -52,10 +49,14 @@ import seaborn as sns
from PIL import Image from PIL import Image
# Source - https://stackoverflow.com/a/53586419
# Posted by korakot, modified by community. See post 'Timeline' for change history
# Retrieved 2026-05-21, License - CC BY-SA 4.0
"""### Loading dataset""" """### Loading dataset"""
# Path to your dataset folder # Path to your dataset folder
dataset_path = "/kaggle/input/skin-cancer-mnist-ham10000" dataset_path = path
# Metadata file # Metadata file
metadata_path = os.path.join(dataset_path, "HAM10000_metadata.csv") metadata_path = os.path.join(dataset_path, "HAM10000_metadata.csv")
@@ -312,6 +313,8 @@ val_generator = val_datagen.flow_from_dataframe(
I will compile the model using the Adam optimizer, binary cross-entropy loss (suitable for binary classification), and track accuracy as a metric. I will compile the model using the Adam optimizer, binary cross-entropy loss (suitable for binary classification), and track accuracy as a metric.
""" """
from tensorflow.keras.optimizers import Adam from tensorflow.keras.optimizers import Adam
model.compile(optimizer=Adam(learning_rate=0.0001), loss='binary_crossentropy', metrics=['accuracy']) model.compile(optimizer=Adam(learning_rate=0.0001), loss='binary_crossentropy', metrics=['accuracy'])