How It Works

(Beta Version)

Pavise is an advanced system designed to assist in medical diagnosis using CT-SCAN images. This system works in a very intuitive and efficient manner, allowing users (such as doctors or healthcare professionals) to analyze medical images faster and more accurately. Here's a more detailed explanation of how Pavise works:

1. User Data Input: The user begins by uploading the CT-SCAN image data they want to diagnose into the system. This image may show a specific organ or area of the body that requires medical attention.

2. Data Processing: After the image data is uploaded, the system sends the image to a server connected to the machine learning model hosted on GitHub. Pavise uses a FASTER CNN (Convolutional Neural Network) architecture, which is very powerful in processing and analyzing medical image data. This architecture is designed to detect important patterns and features in CT-SCAN images with high precision.

3. Automated Detection and Diagnosis: The FASTER CNN model used in Pavise is trained to recognize various patterns and abnormalities in medical images. Once the CT-SCAN image is received, the model processes the image deeply, detecting potential issues such as tumors, blood clots, or other anomalies.

4. Detection Results Display: Once the processing is complete, Pavise will display the diagnosis results to the user in an easy-to-understand format. These results may include highlights or markings on the areas of the CT-SCAN image that show abnormalities, along with estimations or further analysis on the severity or potential issues detected.

5. FASTER CNN Architecture: Pavise relies on FASTER CNN, a neural network architecture that is highly effective for object detection. This architecture optimizes both speed and accuracy in analyzing medical images by utilizing deep convolutional layers to capture important visual features in the image. Compared to traditional CNNs, FASTER CNN also integrates a Region Proposal Network (RPN) that allows the system to detect objects faster and more accurately.

6. Benefits and Applications: Pavise not only speeds up the medical diagnosis process but also provides a more objective and data-driven analysis, reducing the potential for human error. This system is especially beneficial in emergency medical scenarios, where time is crucial, as well as in helping doctors monitor patient progress more effectively.

With a combination of fast processing and high diagnostic accuracy, Pavise makes a significant contribution to the healthcare field, particularly in image-based medical diagnosis, and is expected to improve the overall quality of patient care.

This additional description gives a more comprehensive and engaging overview of how Pavise works, as well as explaining the technology behind the system.