Recent Innovations in Automated Detection and Classification of Diabetic Retinopathy
Wafa Aladawi1, C. Jayakumari2, Sumesh E P3, Vidhyalavanya4
1Wafa Aladawi , MSc IT Student, Middle East College, Muscat, Sultanate of Oman,
1Dr.C. Jayakumari, Faculty, Dept of Computing, Middle East College, Muscat, Sultanate of Oman
3Sumesh E P, Faculty, Dept of Electronic, Middle East College, Muscat, Sultanate of Oman
4Vidhyalavanya, Faculty, Dept of Electronic, Middle East College, Muscat, Sultanate of Oman
Manuscript received on 02 July 2019 | Revised Manuscript received on 09 July 2019 | Manuscript published on 30 August 2019 | PP: 1997-2004 | Volume-8 Issue-10, August 2019 | Retrieval Number: J93060881019/2019©BEIESP | DOI: 10.35940/ijitee.J9306.0881019
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Due to the increasing prevalence of diabetic retinopathy worldwide, it’s an urgent need to develop smart system that help to detect disease using one of the modern technologies. Artificial intelligence is one of the popular techniques nowadays which has the ability to learn from experience and carry out human-like tasks. Large number of researches have been conducted to find out effective medical diagnosis methods for numerous diseases. Likewise, huge number of researches have been done that discuss automated detection and classification of diabetic retinopathy. This paper reviews the existing methodologies, datasets, sensitivity, specificity and classification accuracy in diabetic retinopathy.
Keywords: Diabetes Mellitus; Diabetic Retinopathy; NPDR; PDR; Artificial intelligence; Machine Learning; Image Preprocessing; Deep Learning; CNN; RNN; RBM; Autoencoders; Kaggle; Messidor-2; DRIVE; TensorFlow; Keras; Theano; Sensitivity; Specificity; Accuracy
Scope of the Article: Classification