melanoma.py

# -*- coding: utf-8 -*-
"""Melanoma.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1Wz_engWh9tmPTw05AItL-BUtZD9-2wRc
"""

import numpy as np
import pandas as pd
import seaborn as sb
import matplotlib.pyplot as plt

from glob import glob
from PIL import Image
from sklearn.model_selection import train_test_split

import tensorflow as tf
from tensorflow import keras
from keras import layers
from functools import partial

AUTO = tf.data.experimental.AUTOTUNE
import warnings
warnings.filterwarnings('ignore')

"""# Load & Plot the Data"""

images = glob('drive/MyDrive/ML/train_cancer/*/*.jpg')
len(images)

#replace backslash with forward slash to avoid unexpected errors
images = [path.replace('\\', '/') for path in images]
df = pd.DataFrame({'filepath': images})
df['label'] = df['filepath'].str.split('/', expand=True)[4]
df.head()

df['label_bin'] = np.where(df['label'].values == 'malignant', 1, 0)
df.head()

x = df['label'].value_counts()
plt.pie(x.values,
		labels=x.index,
		autopct='%1.1f%%')
plt.show()

for cat in df['label'].unique():
	temp = df[df['label'] == cat]

	index_list = temp.index
	fig, ax = plt.subplots(1, 4, figsize=(15, 5))
	fig.suptitle(f'Images for {cat} category . . . .', fontsize=20)
	for i in range(4):
		index = np.random.randint(0, len(index_list))
		index = index_list[index]
		data = df.iloc[index]

		image_path = data[0]

		img = np.array(Image.open(image_path))
		ax[i].imshow(img)
plt.tight_layout()
plt.show()

"""# Prepare Dataset"""

features = df['filepath']
target = df['label_bin']

X_train, X_val,\
	Y_train, Y_val = train_test_split(features, target,
									test_size=0.15,
									random_state=10)

X_train.shape, X_val.shape

def decode_image(filepath, label):
    img = tf.io.read_file(filepath)
    img = tf.image.decode_jpeg(img)
    img = tf.image.resize(img, [224, 224])
    img = tf.cast(img, tf.float32) / 255.0
    return img, label

train_ds = (
	tf.data.Dataset
	.from_tensor_slices((X_train, Y_train))
	.map(decode_image, num_parallel_calls=AUTO)
	.batch(32)
	.prefetch(AUTO)
)

val_ds = (
	tf.data.Dataset
	.from_tensor_slices((X_val, Y_val))
	.map(decode_image, num_parallel_calls=AUTO)
	.batch(32)
	.prefetch(AUTO)
)

"""# Model

### Pretrained Model - TF
"""

from tensorflow.keras.applications.efficientnet import EfficientNetB7

pre_trained_model = EfficientNetB7(
	input_shape=(224, 224, 3),
	weights='imagenet',
	include_top=False
)

for layer in pre_trained_model.layers:
	layer.trainable = False

"""### Model - Fully connected (dense) neural network"""

from tensorflow.keras import Model

inputs = layers.Input(shape=(224, 224, 3))
x = layers.Flatten()(inputs)

x = layers.Dense(256, activation='relu')(x)
x = layers.BatchNormalization()(x)
x = layers.Dense(256, activation='relu')(x)
x = layers.Dropout(0.3)(x)
x = layers.BatchNormalization()(x)
outputs = layers.Dense(1, activation='sigmoid')(x)

model = Model(inputs, outputs)

model.compile(
	loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
	optimizer='adam',
	metrics=['AUC']
)

history = model.fit(train_ds,
					validation_data=val_ds,
					epochs=5,
					verbose=1)

model.save('my_model.h5')

"""# Results"""

hist_df = pd.DataFrame(history.history)
hist_df.head()

hist_df['loss'].plot()
hist_df['val_loss'].plot()
plt.title('Loss v/s Validation Loss')
plt.legend()
plt.show()

hist_df['auc'].plot()
hist_df['val_auc'].plot()
plt.title('AUC v/s Validation AUC')
plt.legend()
plt.show()

"""# Model Test with raw JPG"""

def decode_image_wo_label(filepath):
    img = tf.io.read_file(filepath)
    img = tf.image.decode_jpeg(img, channels=3)
    img = tf.image.resize(img, [224,224])
    img = tf.cast(img, tf.float32) / 255.0
    return img

imageVm = 'drive/MyDrive/ML/train_cancer/malignant/10.jpg'

decoded = decode_image_wo_label(imageVm)
decoded.shape
decoded = tf.expand_dims(decoded, axis=0)

predictions = model.predict(decoded)

print(predictions)