|
import streamlit as st |
|
import numpy as np |
|
from keras.preprocessing import image |
|
from keras.models import load_model |
|
|
|
|
|
|
|
model = load_model('braintumordetectmodel.h5') |
|
|
|
|
|
|
|
st.title('Classification of Brain Tumor using CNN') |
|
|
|
st.text("") |
|
|
|
upload_brain_photo = st.file_uploader('Please upload the photo of Brain MRI Image', type=['jpg', 'png']) |
|
|
|
|
|
if upload_brain_photo is not None: |
|
|
|
brain_photo_uploaded = image.load_img(upload_brain_photo, target_size=(180, 180, 3)) |
|
|
|
st.text("") |
|
|
|
col1, col2, col3 = st.columns (3) |
|
|
|
with col1: |
|
st.write (' ') |
|
with col2: |
|
st.image (brain_photo_uploaded, caption='Preview of the uploaded Brain MRI Image', width=250) |
|
with col3: |
|
st.write (' ') |
|
|
|
brain_photo_uploaded_to_arr = image.img_to_array(brain_photo_uploaded) |
|
|
|
brain_photo_uploaded_to_arr = brain_photo_uploaded_to_arr / 255 |
|
|
|
brain_photo_uploaded_to_arr_expand = np.expand_dims(brain_photo_uploaded_to_arr, axis=0) |
|
|
|
prediction = (model.predict(brain_photo_uploaded_to_arr_expand) > 0.5).astype('int32') |
|
|
|
st.text("") |
|
|
|
if prediction[0][0] == 0: |
|
st.markdown("<div style='background-color: green; padding: 8px; border-radius: 10px; text-align: center; color: white; font-size: large'>The Brain MRI imaging that you uploaded shows no signs of any tumor.</div>", unsafe_allow_html=True) |
|
else: |
|
st.markdown("<div style='background-color: red; padding: 8px; border-radius: 10px; text-align: center; color: white; font-size: large'> The brain imaging that you uploaded indeed has signs of a tumor.</div>", unsafe_allow_html=True) |