ith-project/skin_cancer_classification.py

# -*- coding: utf-8 -*-
"""Skin Cancer Classification.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1Nhk-lK1OCihOAxo36qt2e1_WgdLc-EiR

# Skin Cancer Classification
**Output**: Benign / Malignant

**Model**: DenseNet121 (transfer learning)

**Dataset**: Skin Cancer: HAM10000 dataset https://www.kaggle.com/datasets/kmader/skin-cancer-mnist-ham10000/data

| Class | Name                                | Value     |
|-------|-------------------------------------|-----------|
| nv    | Melanocytic nevi (moles)            | Benign    |
| bcc   | Basal cell carcinoma                | Malignant |
| bkl   | Benign keratosis-like lesions       | Benign    |
| df    | Dermatofibroma                      | Benign    |
| vasc  | Vascular lesions                    | Benign    |
| mel   | Melanoma                            | Malignant |
| akiec | Actinic keratoses / Bowen's disease | Benign    |

## 1. Requirements and dataset download
"""

import sys
IN_COLAB = 'google.colab' in sys.modules

import kagglehub

# Download latest version
path = kagglehub.dataset_download("kmader/skin-cancer-mnist-ham10000")

print("Path to dataset files:", path)

# !cp -R /kaggle/input/skin-cancer-mnist-ham10000 /content/skin-cancer-mnist-ham10000

"""## 2. Imports and setup"""

import os
import glob
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

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"""

# Path to your dataset folder
dataset_path = path

# Metadata file
metadata_path = os.path.join(dataset_path, "HAM10000_metadata.csv")

# Load CSV
df = pd.read_csv(metadata_path)

# Show first rows
print(df.head())

# Collect all image paths
image_paths = glob.glob(os.path.join(dataset_path, "**", "*.jpg"), recursive=True)

# Create dictionary:
# key = image_id
# value = full image path
imageid_path_dict = {
    os.path.splitext(os.path.basename(x))[0]: x
    for x in image_paths
}

# Add image path column
df['path'] = df['image_id'].map(imageid_path_dict.get)

# Check
print(df[['image_id', 'path']].head())

"""## 3. Dataset analysis

### Missingness
"""

missing = df['path'].isnull().sum()

print(f"Missing images: {missing}")

"""### Class distribution"""

plt.figure(figsize=(10,5))

sns.countplot(data=df, x='dx', order=df['dx'].value_counts().index)

plt.title("Class Distribution")
plt.xlabel("Diagnosis")
plt.ylabel("Count")
plt.show()

"""### Visualize samples"""

fig, axes = plt.subplots(2, 4, figsize=(10,5))

for i, ax in enumerate(axes.flat):
    sample = df.sample(1).iloc[0]

    img = Image.open(sample['path'])

    ax.imshow(img)
    ax.set_title(sample['dx'])
    ax.axis('off')

plt.tight_layout()
plt.show()

"""### Data distribution"""

plt.figure(figsize=(8,5))

sns.histplot(df['age'].dropna(), bins=20)

plt.title("Age Distribution")
plt.show()

sns.countplot(data=df, x='sex')

plt.title("Sex Distribution")
plt.show()

plt.figure(figsize=(12,5))

sns.countplot(
    data=df,
    x='localization',
    order=df['localization'].value_counts().index
)

plt.xticks(rotation=45)
plt.title("Lesion Localization")
plt.show()

"""### Image sizes"""

sizes = []

for path in df['path'].sample(100):
    img = Image.open(path)
    sizes.append(img.size)

print(pd.Series(sizes).value_counts())

"""## 2. Prepare dataset

### Encoding Binary Labels
"""

# Mapping from dx to benign/malignant
# 0 --> benign
# 1 --> malignant
benign_malignant_dict = {
    'nv': 0,
    'bcc': 1,
    'bkl': 0,
    'df': 0,
    'vasc': 0,
    'mel': 1,
    'akiec': 0
}

# Create new column
df['target'] = df['dx'].map(benign_malignant_dict)

# Preview
print(df[['dx', 'target']].head())

# Convert 'target' column to string type for ImageDataGenerator
train_df['target'] = train_df['target'].astype(str)
val_df['target'] = val_df['target'].astype(str)

print("Train target dtype after conversion:", train_df['target'].dtype)
print("Validation target dtype after conversion:", val_df['target'].dtype)

plt.figure(figsize=(6,3))

sns.countplot(data=df, x='target')

plt.title("Benign vs Malignant Distribution")
plt.xlabel("Lesion Type")
plt.ylabel("Count")
plt.show()

print(df['target'].value_counts())

"""### Train / Validation split"""

from sklearn.model_selection import train_test_split

train_df, val_df = train_test_split(
    df,
    test_size=0.2,
    stratify=df['target'],
    random_state=42
)

print("Train size:", len(train_df))
print("Validation size:", len(val_df))

"""### Class weight (class imbalance)"""

from sklearn.utils.class_weight import compute_class_weight

classes = np.unique(train_df['target'])

weights = compute_class_weight(
    class_weight='balanced',
    classes=classes,
    y=train_df['target']
)

class_weights = dict(enumerate(weights))

print(class_weights)

"""## 3. Build the model"""

import tensorflow as tf
from tensorflow.keras import layers, models
from tensorflow.keras.applications import DenseNet121
from tensorflow.keras.applications.densenet import preprocess_input
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
from tensorflow.keras.models import Model

data_augmentation = tf.keras.Sequential([
    tf.keras.layers.RandomFlip("horizontal"),
    tf.keras.layers.RandomRotation(0.1),
    tf.keras.layers.RandomZoom(0.1),
    tf.keras.layers.RandomContrast(0.1),
])

base_model = DenseNet121(
    weights='imagenet',
    include_top=False,
    input_shape=(224, 224, 3)
)

inputs = tf.keras.Input(shape=(224,224,3))

x = data_augmentation(inputs)

x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(512, activation='relu')(x) # Added another Dense layer
x = Dense(256, activation='relu')(x) # Existing Dense layer
predictions = Dense(1, activation='sigmoid')(x) # Output layer for binary classification

model = Model(inputs=base_model.input, outputs=predictions)

"""## 4. Data Generators

I will prepare data generators for training and validation. The training generator will include data augmentation and preprocessing, while the validation generator will only preprocess the images.
"""

from tensorflow.keras.preprocessing.image import ImageDataGenerator

# Image dimensions
IMG_WIDTH = 224
IMG_HEIGHT = 224

# Data generators
train_datagen = ImageDataGenerator(
    preprocessing_function=preprocess_input,
    rotation_range=20,
    width_shift_range=0.2,
    height_shift_range=0.2,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True,
    fill_mode='nearest'
)

val_datagen = ImageDataGenerator(preprocessing_function=preprocess_input)

# Flow from dataframe
train_generator = train_datagen.flow_from_dataframe(
    dataframe=train_df,
    x_col='path',
    y_col='target',
    target_size=(IMG_WIDTH, IMG_HEIGHT),
    batch_size=32,
    class_mode='binary',
    seed=42
)

val_generator = val_datagen.flow_from_dataframe(
    dataframe=val_df,
    x_col='path',
    y_col='target',
    target_size=(IMG_WIDTH, IMG_HEIGHT),
    batch_size=32,
    class_mode='binary',
    seed=42
)

"""## 5. Compile the Model

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

model.compile(optimizer=Adam(learning_rate=0.0001), loss='binary_crossentropy', metrics=['accuracy'])

"""## 6. Train the Model

I will now train the model using the prepared data generators. I'll also add callbacks for early stopping to prevent overfitting and to save the best model.
"""

from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint

# Callbacks
early_stopping = EarlyStopping(
    monitor='val_loss',
    patience=10,
    restore_best_weights=True
)

model_checkpoint = ModelCheckpoint(
    'best_model.keras',
    monitor='val_accuracy',
    save_best_only=True,
    mode='max'
)

# Train the model
history = model.fit(
    train_generator,
    epochs=3, # You can adjust the number of epochs
    validation_data=val_generator,
    callbacks=[early_stopping, model_checkpoint],
    class_weight=class_weights # Use class weights to handle imbalance
)

"""## X. Evaluation

### Load best model
"""

from tensorflow.keras.models import load_model

best_model = load_model('best_model.keras')

"""### Evaluate on validation set"""

loss, accuracy = best_model.evaluate(val_generator)
print(f"Validation Loss: {loss:.4f}")
print(f"Validation Accuracy: {accuracy:.4f}")

"""### Predictions and Classification Report"""

from sklearn.metrics import classification_report, confusion_matrix

val_generator.reset() # Reset generator to ensure correct order
y_pred_probs = best_model.predict(val_generator)
y_pred = (y_pred_probs > 0.5).astype(int)

y_true = val_generator.classes

print("Classification Report:")
print(classification_report(y_true, y_pred))

"""### Confusion Matrix"""

cm = confusion_matrix(y_true, y_pred)
plt.figure(figsize=(8, 6))
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['Benign', 'Malignant'], yticklabels=['Benign', 'Malignant'])
plt.title('Confusion Matrix')
plt.xlabel('Predicted Label')
plt.ylabel('True Label')
plt.show()

"""### Training History Plots"""

plt.figure(figsize=(12, 5))

# Plot training & validation accuracy values
plt.subplot(1, 2, 1)
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('Model Accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Validation'], loc='upper left')

# Plot training & validation loss values
plt.subplot(1, 2, 2)
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model Loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Validation'], loc='upper left')

plt.tight_layout()
plt.show()