  This paper introduces a paradigm of smartphone application based disease
diagnostics that may completely revolutionise the way healthcare services are
being provided. Although primarily aimed to assist the problems in rendering
the healthcare services during the coronavirus pandemic, the model can also be
extended to identify the exact disease that the patient is caught with from a
broad spectrum of pulmonary diseases. The app inputs Chest X-Ray images
captured from the mobile camera which is then relayed to the AI architecture in
a cloud platform, and diagnoses the disease with state of the art accuracy.
Doctors with a smartphone can leverage the application to save the considerable
time that standard COVID-19 tests take for preliminary diagnosis. The scarcity
of training data and class imbalance issues were effectively tackled in our
approach by the use of Data Augmentation Generative Adversarial Network (DAGAN)
and model architecture based as a Convolutional Siamese Network with attention
mechanism. The backend model was tested for robustness us-ing publicly
available datasets under two different classification
scenarios(Binary/Multiclass) with minimal and noisy data. The model achieved
pinnacle testing accuracy of 99.30% and 98.40% on the two respective scenarios,
making it completely reliable for its users. On top of that a semi-live
training scenario was introduced, which helps improve the app performance over
time as data accumulates. Overall, the problems of generalisability of complex
models and data inefficiency is tackled through the model architecture. The app
based setting with semi live training helps in ease of access to reliable
healthcare in the society, as well as help ineffective research of rare
diseases in a minimal data setting.
