ClaRaF

ClaRaF (Classifier of Radiographic Findings) is a machine learning model developed by the Department of Computer Science and Engineering at the Indian Institute of Technology, Delhi (IIT-D). It is designed to classify chest X-ray images as either normal or abnormal and, if abnormal, identify the presence of specific abnormalities.

ClaRaF uses a deep learning algorithm that analyzes chest X-ray images to classify them as normal or abnormal. If the image is abnormal, it can identify up to 14 different findings, including pneumonia, consolidation, effusion, and nodules.

The accuracy of ClaRaF varies depending on the specific dataset used for testing. However, it has been shown to achieve an accuracy of up to 92% in some studies.

The purpose of ClaRaF is to assist medical professionals in diagnosing chest X-rays by quickly identifying abnormalities and recommending further evaluation or treatment as needed.

Using ClaRaF can save time for medical professionals by quickly analyzing chest X-ray images, potentially leading to earlier diagnosis and treatment. It may also help reduce errors and variability in interpretations by different clinicians.

At present, ClaRaF is primarily a research tool and not yet widely used in clinical practice. However, it has shown promise in initial studies and may become more widely adopted in the future.

ClaRaF is not 100% accurate and should not replace clinical judgment or evaluation by a medical professional. Additionally, the model may not be as effective when used on populations or datasets that differ significantly from those used in its development.

ClaRaF is currently available through its creators at IIT-D for research purposes. As a machine learning model, it requires expertise in programming and data analysis to use effectively.

ClaRaF is currently not open-source software, but its creators at IIT-D may provide access for research purposes upon request.

Future developments for ClaRaF may include expanding the number of findings it can identify, improving its overall accuracy, and integrating it with other healthcare technologies to create more comprehensive diagnostic tools.