| import streamlit as st |
| import numpy as np |
| import tensorflow as tf |
| import pydicom |
| from PIL import Image |
| import io |
|
|
| @st.cache_resource |
| def load_model(): |
| return tf.keras.models.load_model("best_cnn_model.h5") |
|
|
| model = load_model() |
| IMG_SIZE = 128 |
|
|
| st.title("Pneumonia Detection from Chest X-ray") |
| st.write("Upload a chest X-ray image (DICOM format)") |
|
|
| uploaded_file = st.file_uploader("Upload DICOM Image", type=None) |
|
|
| def preprocess_dicom_bytes(dicom_bytes): |
| dicom = pydicom.dcmread(io.BytesIO(dicom_bytes)) |
| img = dicom.pixel_array.astype("float32") |
|
|
| img = img / np.max(img) |
|
|
| img = Image.fromarray((img * 255).astype(np.uint8)) |
| img = img.resize((IMG_SIZE, IMG_SIZE)) |
|
|
| img = np.array(img) / 255.0 |
| img = img.reshape(1, IMG_SIZE, IMG_SIZE, 1) |
| return img |
|
|
| if uploaded_file: |
| try: |
| image = preprocess_dicom_bytes(uploaded_file.read()) |
| prob = model.predict(image)[0][0] |
|
|
| label = "Pneumonia" if prob >= 0.45 else "No Pneumonia" |
|
|
| st.success("Prediction complete") |
| st.write(f"**Predicted Class:** {label}") |
| st.write(f"**Probability:** {prob:.2f}") |
|
|
| except Exception as e: |
| st.error(f"Error processing file: {e}") |
|
|
