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import os
import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.optimizers import Adam
# Set paths to the dataset (adjust paths based on your directory structure)
train_dir = './data/train'
validation_dir = './data/validation'
# Define the CNN model
def create_cnn_model(input_shape=(224, 224, 3)):
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=input_shape))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(1, activation='sigmoid')) # Binary classification (Normal vs Abnormal)
model.compile(optimizer=Adam(), loss='binary_crossentropy', metrics=['accuracy'])
return model
# Create the CNN model
model = create_cnn_model()
# ImageDataGenerator for training and validation
train_datagen = ImageDataGenerator(rescale=1./255, rotation_range=40, width_shift_range=0.2,
height_shift_range=0.2, shear_range=0.2, zoom_range=0.2,
horizontal_flip=True, fill_mode='nearest')
validation_datagen = ImageDataGenerator(rescale=1./255)
# Flow training and validation data from directories
train_generator = train_datagen.flow_from_directory(train_dir, target_size=(224, 224),
batch_size=32, class_mode='binary')
validation_generator = validation_datagen.flow_from_directory(validation_dir, target_size=(224, 224),
batch_size=32, class_mode='binary')
# Train the model
history = model.fit(train_generator, epochs=10, validation_data=validation_generator)
# Save the trained model
model.save('classification_model.h5')
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