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Machine Learning Techniques for Corneal Diseases Diagnosis: A Survey

Samer Kais Jameel

Electrical Electronic and Computer Engineering Department, Aksary University, Aksaray, Turkey

Raparin University, Sulaymaniyah, Iraq

E-mail Address: samer.kais@uor.edu.krd

Corresponding author.

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Sezgin Aydin

Department of Natural and Mathematical Sciences, Engineer Faculty, Tarsus University, Mersin, Turkey

E-mail Address: 01sezgin@gmail.com

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, and

Nebras H. Ghaeb

Biomedical Engineering Department, Al-Khawarezmi Engineering College, University of Baghdad, Baghdad, Iraq

E-mail Address: nebras@kecbu.uobaghdad.edu.iq

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https://doi.org/10.1142/S0219467821500169Cited by:2 (Source: Crossref)

Abstract

Machine learning techniques become more related to medical researches by using medical images as a dataset. It is categorized and analyzed for ultimate effectiveness in diagnosis or decision-making for diseases. Machine learning techniques have been exploited in numerous researches related to corneal diseases, contribution to ophthalmologists for diagnosing the diseases and comprehending the way automated learning techniques act. Nevertheless, confusion still exists in the type of data used, whether it is images, data extracted from images or clinical data, the course reliant on the type of device for obtaining them. In this study, the researches that used machine learning were reviewed and classified in terms of the kind of utilized machine for capturing data, along with the latest updates in sophisticated approaches for corneal disease diagnostic techniques.

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