Corneal Ulcer Region Detection With Semantic Segmentation Using Deep Learning (딥러닝과 의미론적 분할 기법을 이용한 각막궤양 영역 검출)

Jinhyuk Im (임진혁); Daewon Kim (김대원)

doi:10.9708/jksci.2022.27.09.001

Corneal Ulcer Region Detection With Semantic Segmentation Using Deep Learning

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2022, 27(9), pp.1-12
DOI : 10.9708/jksci.2022.27.09.001
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : July 4, 2022
Accepted : August 3, 2022
Published : September 30, 2022

Jinhyuk Im ¹, Daewon Kim ¹

¹단국대학교

Accredited

ABSTRACT

Traditional methods of measuring corneal ulcers were difficult to present objective basis for diagnosis because of the subjective judgment of the medical staff through photographs taken with special equipment. In this paper, we propose a method to detect the ulcer area on a pixel basis in corneal ulcer images using a semantic segmentation model. In order to solve this problem, we performed the experiment to detect the ulcer area based on the DeepLab model which has the highest performance in semantic segmentation model. For the experiment, the training and test data were selected and the backbone network of DeepLab model which set as Xception and ResNet, respectively were evaluated and compared the performances. We used Dice similarity coefficient and IoU value as an indicator to evaluate the performances. Experimental results show that when ‘crop & resized’ images are added to the dataset, it segment the ulcer area with an average accuracy about 93% of Dice similarity coefficient on the DeepLab model with ResNet101 as the backbone network. This study shows that the semantic segmentation model used for object detection also has an ability to make significant results when classifying objects with irregular shapes such as corneal ulcers. Ultimately, we will perform the extension of datasets and experiment with adaptive learning methods through future studies so that they can be implemented in real medical diagnosis environment.

KEYWORDS

Semantic Segmentation, Corneal Ulcer, DeepLab, Xception, ResNet, Dice Similarity Coefficient (DSC), IoU

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* References for papers published after 2023 are currently being built.

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Corneal Ulcer Region Detection With Semantic Segmentation Using Deep Learning

ABSTRACT

KEYWORDS

Citation status

* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Corneal Ulcer Region Detection With Semantic Segmentation Using Deep Learning

ABSTRACT

KEYWORDS

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* References for papers published after 2023 are currently being built.