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bbecad4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 | import streamlit as st
import tensorflow as tf
from tensorflow import keras
import cv2
st.title(":brain: Brain Tumor Detection (MRI)")
vggnet_19= keras.models.load_model("CNN_MODEL_0_9687.h5")
vggnet_16 = keras.models.load_model("CNN_MODEL_0_96414.h5")
resnet_152 = keras.models.load_model("CNN_MODEL_0_9794.h5")
mobilenet_v3= keras.models.load_model("CNN_MODEL_0_9771.h5")
def check_accuracy(model,img_input):
y_pred = model.predict(img_input).argmax(axis=1)
prediction = model.predict(img_input)
if y_pred[0] == 0:
return "Target: glioma_tumor",prediction.max()
elif y_pred[0] == 1:
return "Target: meningioma_tumor",prediction.max()
elif y_pred[0] == 2:
return "Target: no_tumor",prediction.max()
else:
return "Target: pituitary_tumor",prediction.max()
with st.container():
file = st.file_uploader("Upload the MRI Image")
if file is not None:
print(file)
save_image_path = "./upload_images/"+file.name
with open(save_image_path,"wb") as f:
f.write(file.getbuffer())
img = cv2.imread(save_image_path)
col1,col2 = st.columns(2)
with st.container():
if file is not None:
with col1:
st.write("Original Image:")
st.image(file)
with col2:
st.write("Processed Image:")
st.image(cv2.resize(img,(224,224)))
else:
st.error("First upload image")
with st.container():
if file is not None:
img = cv2.resize(img,(224,224))
img_input = img.reshape((1,224,224,3))
vgg19,v19 = check_accuracy(vggnet_19,img_input)
vgg16,v16 = check_accuracy(vggnet_16,img_input)
resnet,r152 = check_accuracy(resnet_152,img_input)
mobilenet,mv3 = check_accuracy(mobilenet_v3,img_input)
st.subheader(vgg19+" for"+ " VGGNET-19")
st.write("accuracy of the image",v19)
st.subheader(vgg16+" for"+ " VGGNET-16")
st.write("accuracy of the image",v16)
st.subheader(resnet+" for"+ " RESNET-152")
st.write("accuracy of the image",r152)
st.subheader(mobilenet+" for"+ " MOBILENET-V3")
st.write("accuracy of the image",mv3)
# st.sidebar.title("Choose the Model:")
# option = st.sidebar.selectbox("Select model",["model1","model2"])
# if option == "model1":
# st.header("Model 1")
# file = st.file_uploader("Upload the MRI Image")
# if file is not None:
# print(file)
# save_image_path = "./upload_images/"+file.name
# with open(save_image_path,"wb") as f:
# f.write(file.getbuffer())
# img = cv2.imread(save_image_path)
# img = cv2.resize(img,(250,250))
# img_input = img.reshape((1,250,250,3))
# y_pred = model1.predict(img_input).argmax(axis=1)
# if y_pred[0] == 0:
# st.write("Target: glioma_tumor")
# elif y_pred[0] == 1:
# st.write("Target: meningioma_tumor")
# elif y_pred[0] == 2:
# st.write("Target: no_tumor")
# else:
# st.write("Target: pituitary_tumor")
# else:
# st.warning("Not Entered image")
# if option == "model2":
# st.header("Model 2")
# file = st.file_uploader("Upload the MRI Image")
# if file is not None:
# print(file)
# save_image_path = "./upload_images/"+file.name
# with open(save_image_path,"wb") as f:
# f.write(file.getbuffer())
# img = cv2.imread(save_image_path)
# img = cv2.resize(img,(250,250))
# img_input = img.reshape((1,250,250,3))
# y_pred = model2.predict(img_input).argmax(axis=1)
# if y_pred[0] == 0:
# st.write("Target: glioma_tumor")
# elif y_pred[0] == 1:
# st.write("Target: meningioma_tumor")
# elif y_pred[0] == 2:
# st.write("Target: no_tumor")
# else:
# st.write("Target: pituitary_tumor")
# else:
# st.warning("Not Entered image")
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