model.py

import numpy as np
import cv2
import pandas as pd
import os
from glob import glob
np.random.seed(123)
from tensorflow.keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.metrics import categorical_crossentropy
from tensorflow.keras.callbacks import ReduceLROnPlateau
from sklearn.model_selection import train_test_split
from tensorflow.keras import Sequential
from tensorflow.keras.preprocessing.image import ImageDataGenerator


base_dir = os.path.join('..', '/content/train')
imageid_path_dict = {os.path.splitext(os.path.basename(x))[0]:x for x in glob(os.path.join(base_dir, '*', '*.jpg'))}

lesion_type_dict = {
    'nv': 0,
    'mel': 1,
    'bkl': 2,
    'bcc': 3,
    'akiec': 4,
    'vasc': 5,
    'df': 6
}

skin_df = pd.read_csv(os.path.join(base_dir, 'HAM10000_metadata.csv'))
skin_df['path'] = skin_df['image_id'].map(imageid_path_dict.get)
skin_df['cell_type'] = skin_df['dx'].map(lesion_type_dict.get)
skin_df['image'] = skin_df['path'].map(lambda x: cv2.resize(cv2.imread(x, cv2.IMREAD_GRAYSCALE),(75,75)))

data = pd.DataFrame(columns=['image','lable'])
data['image'] = skin_df['image']
data['lable'] = skin_df['cell_type']
data.dropna(inplace=True)

x_train = np.asarray(data['image'].tolist()).reshape(-1,75,75,1)
y_train = data['lable']
y_train = pd.get_dummies(y_train)
x_train, x_validate, y_train, y_validate = train_test_split(x_train, y_train, test_size = 0.1, random_state = 125)

input_shape = (75, 75,1)
num_classes = 7

datagen = ImageDataGenerator(
        rotation_range=10,  # randomly rotate images in the range (degrees, 0 to 180)
        zoom_range = 0.1, # Randomly zoom image 
        width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)
        height_shift_range=0.1 )


model = Sequential()

#input
model.add(Conv2D(32, kernel_size=(3, 3),activation='relu',padding = 'Same',input_shape=input_shape))

#hidden1
model.add(Conv2D(32,kernel_size=(3, 3), activation='relu',padding = 'Same',))
model.add(MaxPool2D(pool_size = (2, 2)))
model.add(Dropout(0.25))

#hidden2
model.add(Conv2D(64, (3, 3), activation='relu',padding = 'Same'))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Dropout(0.3))

#hidden3
model.add(Conv2D(64, (3, 3), activation='relu',padding = 'Same'))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Dropout(0.3))

model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.4))
model.add(Dense(num_classes, activation='softmax'))

optimizer = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)
model.compile(optimizer = optimizer , loss = "categorical_crossentropy", metrics=["accuracy"])
learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc', 
                                            patience=3, 
                                            verbose=1, 
                                            factor=0.5, 
                                            min_lr=0.00001)

datagen.fit(x_train)
epochs = 50 
batch_size = 10

history = model.fit_generator(datagen.flow(x_train,y_train, batch_size=batch_size),
                              epochs = epochs, validation_data = (x_validate,y_validate),
                              verbose = 1, steps_per_epoch=x_train.shape[0] // batch_size
                              , callbacks=[learning_rate_reduction])


model.save("mode_skincancer.h5")