Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Detecting Keratoconus Using Convolutional Neural Network on Smartphone

Version 1 : Received: 9 April 2020 / Approved: 16 April 2020 / Online: 16 April 2020 (12:38:42 CEST)

How to cite: Rostami, S. Detecting Keratoconus Using Convolutional Neural Network on Smartphone. Preprints 2020, 2020040271. https://doi.org/10.20944/preprints202004.0271.v1 Rostami, S. Detecting Keratoconus Using Convolutional Neural Network on Smartphone. Preprints 2020, 2020040271. https://doi.org/10.20944/preprints202004.0271.v1

Abstract

Nowadays smartphone utilization for disease diagnosis and remote health care applications has become promising due to their ubiquity. Here, a novel convolutional neural network method for detecting keratoconus that is wholly implemented on a smartphone is proposed. The proposed method provides accurate detection of over 72.9% for all stages of keratoconus. Preliminary results indicate 90%, 83%, 64% and 52% detection rate for severe, advanced, moderate and mild stages of disease, respectively.

Keywords

Keratoconus; smartphone; cornea; convolutional neural network

Subject

Computer Science and Mathematics, Computer Science

Comments (0)

Comment 1
Received: 23 September 2021
Commenter: Parisa Forghani
The commenter has declared there is no conflict of interests.
Comment: Hello,
I hope that everything is going well with you.

My name is Parisa Forghani, and I am interested in using the database you have used in this paper.

I am working in ARAS Lab, under the supervision of Dr. Hamid D. Taghirad. My work focuses on Artificial Intelligence ways to help Corneal Diseases like Keratoconus. Therefore, your dataset would be a great help. I assure you that I would include the proper reference if I used your data.

Best regards,
Parisa.

aras.kntu.ac.ir scholar.google.com/citations?user=5O5cQHsAAAAJ&hl=en
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