Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/3157
Title: UveaTrack: Uveitis Eye Disease Prediction and Detection with Vision Function Calculation and Risk Analysis Publisher: IEEE Cite This PDF
Authors: Perera, B. D. K
Wickramarathna, W.A.A.I.
Chandrasiri, S
Wanniarachchi, W.A.P.W
Dilshani, S.H.N
Pemadasa, N
Keywords: Deep Learning
Detection
Image Processing
Machine Learning
Prediction
Risk Analysis
Uveitis
Vision Function
Issue Date: 15-Oct-2022
Publisher: Institute of Electrical and Electronics Engineers
Citation: B. D. K. Perera, W. A. A. I. Wickramarathna, S. Chandrasiri, W. A. P. W. Wanniarachchi, S. H. N. Dilshani and N. Pemadasa, "UveaTrack: Uveitis Eye Disease Prediction and Detection with Vision Function Calculation and Risk Analysis," 2022 IEEE 13th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), Vancouver, BC, Canada, 2022, pp. 0088-0093, doi: 10.1109/IEMCON56893.2022.9946505.
Series/Report no.: 2022 IEEE 13th Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2022;Pages 88 - 93
Abstract: Uveitis is an inflammatory infection that affects uvea tissue, the middle layer of the eyewall. It can result in swelling or damage to the eye and lead to vision impairments or blindness. Most Uveitis symptoms are associated with many other diseases localized to the eye. Thus, it is hard to determine the responsible symptoms for uveitis. Consequently, early detection of this disease can prevent a perilous situation in the future. The initial motivation behind the design of this mobile application is to help accurately diagnose uveitis with minimal time and effort and thereby minimize the shortage of human specialists in this field. The 'UveaTrack' is a hybrid mobile application that enables the keep tracking of uveitis eye illness and uses machine learning (ML) algorithms, deep learning (DL) architectures, and image processing techniques for developing the system. The 'UveaTrack' application could be able to achieve an average accuracy of more than 85% and had produced overall better results. Furthermore, the 'UveaTrack' application can use as a valuable instructional tool for freshly graduated clinicians, supporting their work with patients and assisting them in making diagnostics conclusions.
URI: https://rda.sliit.lk/handle/123456789/3157
ISBN: 978-166546316-4
Appears in Collections:Department of Information Technology

Files in This Item:
File Description SizeFormat 
UveaTrack_Uveitis_Eye_Disease_Prediction_and_Detection_with_Vision_Function_Calculation_and_Risk_Analysis.pdf
  Until 2050-12-31
553.75 kBAdobe PDFView/Open Request a copy


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.