Assessing Automated Machine Learning service to detect COVID-19 from
X-Ray and CT images: A Real-time Smartphone Application case study
The purpose of our study was to evaluate Microsoft Cognitive Service to
detect COVID19 induced pneumonia and ordinary viral or bacterial
infection in Lung using X-Ray and CT scan images. We have used Datasets
from a recognized and trusted source to build our model. The primary
objective is a Smartphone based on device real-time inference system. In
this case, the model would run by a mobile device’s System on Chip (SoC)
and will not require an internet connection for inference with zero
latency. This system would be particularly suitable for rural areas of
developing countries where internet connection is poor or not available.
The secondary solution would be a web portal running the inference
through REST API from Custom Vision.
Now, given the nature of The Severe Acute Respiratory Syndrome
Coronavirus 2 (SARS-COV-2), which causes respiratory disease as a novel
one, the majority of the radiologists are not acquainted enough to
detect the virus-related changes from the X-Ray. Moreover, the
morphology of COVID-19 and common Pneumonia are hard to differentiate
from X-Ray alone without the patient’s symptoms by a radiologist.
Here, AI comes into play with the role of an expert assistant. It is
much faster and efficient to train a machine over thousands of labeled
training data to observe and detect subtle differences between various
X-Ray images to train its Artificial Neural Network and classify them
quickly which is otherwise not possible by a human eye. A Radiologist
can use the app to primarily identify the X-Ray in question and combine
it with his/her medical expertise along with the patient’s case history
before in conjunction with tests like RT PCR/Antibody.