  Although deep learning research and applications have grown rapidly over the
past decade, it has shown limitation in healthcare applications and its
reachability to people in remote areas. One of the challenges of incorporating
deep learning in medical data classification or prediction is the shortage of
annotated training data in the healthcare industry. Medical data sharing
privacy issues and limited patient population size can be stated as some of the
reasons for training data insufficiency in healthcare. Methods to exploit deep
learning applications in healthcare have been proposed and implemented in this
dissertation.
  Traditional diagnosis of diabetic retinopathy requires trained
ophthalmologists and expensive imaging equipment to reach healthcare centres in
order to provide facilities for treatment of preventable blindness. Diabetic
people residing in remote areas with shortage of healthcare services and
ophthalmologists usually fail to get periodical diagnosis of diabetic
retinopathy thereby facing the probability of vision loss or impairment. Deep
learning and mobile application development have been integrated in this
dissertation to provide an easy to use point-of-care smartphone based diagnosis
of diabetic retinopathy. In order to solve the challenge of shortage of
healthcare centres and trained ophthalmologists, the standalone diagnostic
service was built so as to be operated by a non-expert without an internet
connection. This approach could be transferred to other areas of medical image
classification.
