Pink Tech Design develops AI Algorithm to classify X-rays to identify COVID 19 patients

X-rays and CT (computed tomography) scans can be an equally effective method to detect COVID-19 as well as present greater detail and accuracy in the results

Pink Tech Design an IT innovation company has developed an AI algorithm to classify X-Rays of COVID19 positive patients. With the development being successful, it can pre-screen patients who need COVID19 confirmatory tests.

The company got access to a dataset of COVID-19 positive X Rays and COVID-19 negative X Rays released by Kaggle, they processed the data and trained their COVID-19 detector with Keras and TensorFlow. After many optimisations, the model gave 90 per cent accuracy with a sensitivity of 0.8 and specificity of 1.0. Though these are initial results, the development has made it clear that X-rays and CT (computed tomography) scans can be an equally effective method to detect COVID-19 as well as present greater detail and accuracy in the results.

Speaking about the development, Dr Kanav Kahol, CEO, PinkTech Design said, “The project has led us to some extremely promising results and we are keen to build on this success rapidly to help in the fight against Coronavirus. The AI model employed in the study is able to predict results with great accuracy. However, our research team continues to strive for even more robust and reliable results. I am incredibly proud of my team for developing this. These are times of quick action and we are happy to provide this model free of cost to any healthcare institute.”

The pandemic has caused a devastating effect on all human lives, livelihood and the global economy which is turning catastrophic in many ways. Hence, it is extremely critical to detect positive cases as early as possible to control the spread of the virus further. It is evident that the application of advanced Artificial Intelligence (AI) techniques combined with radiological imaging can be assistive to overcome many challenges in accurate and early detection of this disease.