Spaces:
Sleeping
Sleeping
File size: 3,587 Bytes
aea3610 ba29f94 aea3610 ba29f94 aea3610 ba29f94 aea3610 fe6e7b2 aea3610 fe6e7b2 ba29f94 aea3610 ba29f94 fe6e7b2 aea3610 fe6e7b2 ba29f94 fe6e7b2 aea3610 ba29f94 aea3610 fe6e7b2 ba29f94 fe6e7b2 aea3610 fe6e7b2 aea3610 |
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 |
import streamlit as st
import easyocr
from transformers import AutoModelForTokenClassification, AutoTokenizer, pipeline
from PIL import Image
import numpy as np
# Initialize EasyOCR for text extraction
reader = easyocr.Reader(['en'])
# Load ClinicalBERT for NER (Named Entity Recognition)
model_name = "emilyalsentzer/Bio_ClinicalBERT"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForTokenClassification.from_pretrained(model_name)
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
# Mock function for validating test results
def validate_medical_value(test_name, test_value):
reference_ranges = {
"Blood Pressure": (90, 120), # Example range: systolic
"Glucose": (70, 100), # Normal range for glucose in mg/dL
}
if test_name in reference_ranges:
min_range, max_range = reference_ranges[test_name]
if test_value < min_range:
return f"{test_name} is low."
elif test_value > max_range:
return f"{test_name} is high."
else:
return f"{test_name} is within the normal range."
else:
return f"{test_name} range is unknown."
# Function to extract text from image
def extract_text_from_image(image):
result = reader.readtext(image)
text = ' '.join([item[1] for item in result])
return text
# Function to check image clarity (basic approach using edge detection)
def check_image_clarity(image):
# Convert image to grayscale
gray_image = image.convert("L")
image_np = np.array(gray_image)
# Simple edge detection (Laplacian method)
laplacian_var = cv2.Laplacian(image_np, cv2.CV_64F).var()
# Threshold for determining clarity
return laplacian_var > 100 # This threshold can be adjusted
# Function to extract medical entities (test results) from text
def extract_medical_values(text):
entities = nlp(text)
medical_data = {}
for entity in entities:
if entity['entity_group'] in ['LAB_RESULT', 'DISEASE', 'MEDICATION']: # Modify based on actual labels
medical_data[entity['word']] = entity['score']
return medical_data
# Function to analyze medical report
def analyze_report(image):
# Step 1: Check image clarity
if not check_image_clarity(image):
return "Image is unclear, please upload a clearer image."
# Step 2: Extract text from the medical report image
text = extract_text_from_image(image)
# Step 3: Extract medical values using ClinicalBERT
medical_values = extract_medical_values(text)
analysis_results = []
for test_name, test_value in medical_values.items():
# Step 4: Validate test value against normal range
validation_result = validate_medical_value(test_name, test_value)
analysis_results.append(validation_result)
return analysis_results
# Streamlit UI
st.title("Medical Report Analyzer")
st.write("Upload your medical report image to analyze the test results.")
# Upload the image
uploaded_image = st.file_uploader("Upload Image", type=["jpg", "png", "jpeg"])
if uploaded_image is not None:
# Open the uploaded image
image = Image.open(uploaded_image)
# Show image for verification
st.image(image, caption="Uploaded Medical Report", use_column_width=True)
# Analyze the report
analysis_results = analyze_report(image)
# Display results
if isinstance(analysis_results, list):
for result in analysis_results:
st.write(result)
else:
st.write(analysis_results)
|