Machine Learning and Deep Learning Techniques, Features and Obstacles in the Cataract Diagnosis
Isaac Ñuflo1, Franco Mecca2, Lenis Wong3
1Isaac Ñuflo*, Faculty of Software and Computer Engineering, National University of San Marcos, Lima, Peru.
2Franco Mecca, Faculty of Software and Computer Engineering, National University of San Marcos, Lima, Peru.
3Lenis Wong, Faculty of Software and Computer Engineering, National University of San Marcos, Lima, Peru.
Manuscript received on August 01, 2020. | Revised Manuscript received on August 05, 2020. | Manuscript published on September 30, 2020. | PP: 87-92 | Volume-9 Issue-3, September 2020. | Retrieval Number: 100.1/ijrte.C4283099320 | DOI: 10.35940/ijrte.C4283.099320
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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: Cataract is a degenerative condition that, according to estimations, will rise globally. Even though there are various proposals about its diagnosis, there are remaining problems to be solved. This paper aims to identify the current situation of the recent investigations on cataract diagnosis using a framework to conduct the literature review with the intention of answering the following research questions: RQ1) Which are the existing methods for cataract diagnosis? RQ2) Which are the features considered for the diagnosis of cataracts? RQ3) Which is the existing classification when diagnosing cataracts? RQ4) And Which obstacles arise when diagnosing cataracts? Additionally, a cross-analysis of the results was made. The results showed that new research is required in: (1) the classification of “congenital cataract” and, (2) portable solutions, which are necessary to make cataract diagnoses easily and at a low cost.
Keywords: Cataract Diagnosis, Image Processing, Ophthalmology, Machine Learning Techniques, Deep Learning Techniques.