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Predict based upon the user data.
Classify health symptoms to suggest possible diagnoses
Detect tumors in brain images
AI-Powered Diagnosis & Treatment Assistant
Mammography Images Classification
Evaluate cancer risk based on cell measurements
Visualize DICOM files and run AI models for radiation therapy
Analyze eye images to identify ocular diseases
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Classify and assess severity of lung conditions from chest X-rays
Classify lung cancer cases from images
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Flask is a micro web framework written in Python. It is lightweight, flexible, and ideal for building small applications or APIs, particularly in the Medical Imaging domain where it can be used for tasks like MRI analysis to detect brain tumors.
• Lightweight: Flask has a small codebase and is easy to use without unnecessary features. • Flexible: It supports extensions and is highly customizable for various application needs. • Modular: Built with a modular design, Flask allows developers to build applications in a structured manner. • Unit Testing: Flask comes with built-in support for unit testing, making it developer-friendly. • RESTful API Ready: It simplifies the creation of RESTful web services.
Using Flask involves a few simple steps:
pip install flask
in your terminal.from flask import Flask
app = Flask(__name__)
@app.route("/")
def home():
return "Hello, Flask!"
app.run()
.1. How do I install Flask?
Flask can be installed using pip: pip install flask
.
2. Is Flask suitable for building large enterprise applications?
Flask is best suited for small to medium-sized applications. For large enterprise applications, Django might be more appropriate.
3. How can I handle large MRI files in Flask?
To handle large MRI files, use streaming uploads or background processing to ensure efficient memory usage and performance.
4. Are there extensions available for Flask?
Yes, Flask has a wide range of extensions for databases, forms, caching, and more, which can be easily integrated into your application.