train_model.py

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')