def preprocess_xray(image_path, target_size=(224, 224),
                    train_mean=0.482, train_std=0.236):
    """
    Full preprocessing pipeline for chest X-ray images.
    Applies all six pillars in order.
    """
    # Pillar 4: Validate first — skip corrupted files
    if not is_valid_image(image_path):
        return None
    
    image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
    
    # Pillar 5: Resize with aspect ratio preserved
    image = resize_with_padding(image, target_size)
    
    # Pillar 6: Gentle denoising
    image = cv2.medianBlur(image, 3)
    
    # Pillar 3: Enhance contrast to highlight lung texture
    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
    image = clahe.apply(image)
    
    # Pillar 1: Scale to [0, 1]
    image = image.astype(np.float32) / 255.0
    
    # Pillar 2: Normalize using training set statistics
    image = (image - train_mean) / train_std
    
    return image