Open AccessEvaluating the Viability of a Smartphone-Based Annotation Tool for Faster and Accurate Image Labelling for Artificial Intelligence in Diabetic Retinopathy
Author(s) -
Arvind Kumar Morya,
Jaitra Gowdar,
Abhishek Kaushal,
Nachiket Makwana,
Saurav Biswas,
Puneeth Raj,
Shabnam Singh,
Sharat Hegde,
Raksha Vaishnav,
Sharan Shetty,
S P Vidyambika,
Vedang Shah,
Sabita Paul,
Sonali Muralidhar,
Girish Velis,
Winston Padua,
Tushar Waghule,
Nazneen Nazm,
Sangeetha Jeganathan,
Ayyappa Reddy Mallidi,
Dona Susan John,
Sagnik Sen,
Sandeep Choudhary,
Nishant Parashar,
Bhavana Sharma,
Pankaja Raghav,
Raghuveer Udawat,
Sampat Ram,
Umang P Salodia
Publication year - 2021
Publication title -
clinical ophthalmology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.025
H-Index - 56
eISSN - 1177-5483
pISSN - 1177-5467
DOI - 10.2147/opth.s289425
Subject(s) - computer science , artificial intelligence , software portability , machine learning , annotation , automatic image annotation , pattern recognition (psychology) , image processing , image (mathematics) , programming language
Deep Learning (DL) and Artificial Intelligence (AI) have become widespread due to the advanced technologies and availability of digital data. Supervised learning algorithms have shown human-level performance or even better and are better feature extractor-quantifier than unsupervised learning algorithms. To get huge dataset with good quality control, there is a need of an annotation tool with a customizable feature set. This paper evaluates the viability of having an in house annotation tool which works on a smartphone and can be used in a healthcare setting.