SBC Based Cataract Detection System using Deep Convolutional Neural Network with Transfer Learning Algorithm
Kenneth C. Karamihan1, Ivan Dave F. Agustino2, Ronnick Bien B. Bionesta3, Ferangelo C. Tuason4, Steven Valentino E. Arellano5, Phillip Amir M. Esguerra6
1Kenneth C. Karamihan, Department of Computer Engineering, College of Engineering, University of Perpetual Help System Laguna, City of Biñan, Laguna, 4024 Philippines.
2Ivan Dave F. Agustino, Department of Computer Engineering, College of Engineering, University of Perpetual Help System Laguna, City of Biñan, Laguna, 4024 Philippines.
3Ronnick Bien B. Bionesta, Department of Computer Engineering, College of Engineering, University of Perpetual Help System Laguna, City of Biñan, Laguna, 4024 Philippines.
4Ferangelo C. Tuason, Department of Computer Engineering, College of Engineering, University of Perpetual Help System Laguna, City of Biñan, Laguna, 4024 Philippines.
5Steven Valentino E. Arellano, Department of Computer Engineering, College of Engineering, University of Perpetual Help System Laguna, City of Biñan, Laguna, 4024 Philippines.
6Phillip Amir M. Esguerra, Department of Electronics Engineering, College of Engineering, University of Perpetual Help System Laguna, City of Biñan, Laguna, 4024 Philippines.
Manuscript received on 15 March 2019 | Revised Manuscript received on 20 March 2019 | Manuscript published on 30 July 2019 | PP: 4605-4613 | Volume-8 Issue-2, July 2019 | Retrieval Number: B3368078219/19©BEIESP | DOI: 10.35940/ijrte.B3368.078219
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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: This Raspberry Pi Single Board Computer-Based Cataract Detection System using Deep Convolutional Neural Network through Google Net Transfer Learning and MATLAB digital image processing paradigm based on Lens Opacities Classification System III with Python application, which would capture the image of the eyes of cataract patients to detect the type of cataract without using dilating drops. Additionally, the system could also determine the severity, grade, color or area, and hardness of cataract. It would also display, save, search and print the partial diagnosis that can be done to the patients. Descriptive quantitative research, Waterfall System Development Life Cycle and Evolutionary Prototyping Models was used as the methodologies of this study. Cataract patients and ophthalmologists of one of the eye clinics in City of Biñan, Laguna, as well as engineers and information technology professionals tested the system and also served as respondents to the conducted survey. Obtained results indicated that the detection of cataract and its characteristics using the system were accurate and reliable, which has a significant difference from the current eye examination for cataract. Generally, this would be a modern cataract detection system for all Cataract patients.
Index Terms: Cataract Detection, Deep Convolutional Neural Network, Digital Image Processing, Transfer Learning.
Scope of the Article: Image Processing and Pattern Recognition