Mayo Clinic Television Iproject. Jayanth Reddy Bogasamudram,

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Mayo Clinic Television iProject
JAYANTH REDDY BOGASAMUDRAM, Department of Software Engineering
1209270448, jbogasam@asu.edu
ARIRZONA STATE UNIVERSITY
Mayo clinic is a nonprofit medical research group focusing on solving medical problems maintaining finest medical practices. Long waiting appointments and schedules always seem to be a problem for patients who seek immediate medical care especially cases when problem related to sensitive body parts are intense and need immediate care. To achieve better time management and proper medical care, an IOS app is designed to deliver the results of examination report directly to physicians and technicians at Mayo clinic, thus delivering the immediate results. The primary focus of the app is to
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JSON format & API for the IOS application is discussed in API’s and JSON format section. Phase-2 contains visual recognition API’s, deep learning vs neural networks and proof of concept model. Visual recognition API’s discuss about various prominent API’s developed by Microsoft, Amazon and IBM Watson. This section also contains various technologies considered while developing these API’s. Deep learning vs neural networks section explains the reason why deep learning is implemented in these API’s and how deep learning improves the performance of image recognition. The Architecture and proof of concept explains how the model can be implemented in IOS application.
2 INITIAL DESIGN

Fig. 1. Side view and front view of initial design with portable case.
Initially, ophthalmologists required a way to take mobile images of the eye and produce image to clinicians that helps diagnose eye issues. The primary goal of this design is to reduce
1. Physicians travel time during off hours.
2. Patient wait time.
3. Usage and cost of the application.
The ASU team was tasked to develop a portable camera that could be used by technicians in areas where large, complex and expensive instrumentation are needed to perform eye examinations and provide accurate results. The team performed extensive research and concluded that apple camera is good fit for the research. Understanding the compatibility

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