A CNN Approach to Central Retinal Vein Occlusion Detection

• Autorzy: Bala Jayanthi Rajee , Sindha Mohamed Mansoor Roomi , Sahayam Jency , Govindharaj Praveena , Rakesh Karthika Priya

Identyfikatory

DOI

10.24425/ijet.2023.146508

• Języki publikacji: EN

Abstrakty

EN

In the field of medicine there is a need for the automatic detection of retinal disorders. Blindness in older persons is primarily caused by Central Retinal Vein Occlusion (CRVO). It results in rapid, irreversible eyesight loss, therefore, it is essential to identify and address CRVO as soon as feasible. Hemorrhages, which can differ in size, pigment, and shape from dot-shaped to flame hemorrhages, are one of the earliest symptoms of CRVO. The early signs of CRVO are, hemorrhages, however, so mild that ophthalmologists must dynamically observe such indicators in the retina image known as the fundus image, which is a challenging and time-consuming task. It is also difficult to segment hemorrhages since the blood vessels and hemorrhages (HE) have the same color properties also there is no particular shape for hemorrhages and it scatters all over the fundus image. A challenging study is needed to extract the characteristics of vein deformability and dilatation. Furthermore, the quality of the captured image affects the efficacy of feature Identification analysis. In this paper, a deep learning approach for CRVO extraction is proposed.

Słowa kluczowe

EN

Blood vessels; segmentation; Features; CRVO; deep learning

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2023
• Tom: Vol. 69, No. 3
• Strony: 565--570
• Opis fizyczny: Bibliogr. 15 poz., rys., tab., wykr.

Twórcy

autor

Bala Jayanthi Rajee

• Thiagarajar College of Engineering, Madurai, India

autor

Sindha Mohamed Mansoor Roomi

• Thiagarajar College of Engineering, Madurai, India

autor

Sahayam Jency

• Thiagarajar College of Engineering, Madurai, India

autor

Govindharaj Praveena

• Thiagarajar College of Engineering, Madurai, India

autor

Rakesh Karthika Priya

• Thiagarajar College of Engineering, Madurai, India

Bibliografia

• [1] Bismita Choudhury, Patrick H.H. and Valliappan Raman, "Automated Detection of Central Retinal Vein Occlusion Using Convolutional Neural Network", Springer International Publishing AG, 2017 https://doi.org/10.1007/978-3-319-63917-8_1
• [2] N. Rajapaksha, L. Ranathunga, K.M.P.K Bandara, "Detection of Central Retinal Vein Occlusion Using Guided Salient Features", IEEE-Digital Image Computing: Techniques and Applications (DICTA). 2019 https://doi.org/10.1109/DICTA47822.2019.8945864
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• [4] Ganesan P, M. Ganesh, L.M.I. Leo Joseph, V. Kalist, "Central Retinal Vein Occlusion: An Approach for the Detection and Extraction of Retinal Blood Vessels", 2018 Journal of Pharmaceutical Sciences and Research 2018, https://www.jpsr.pharmainfo.in/Documents/Volumes/vol10Issue01/jpsr10011840.pdf
• [5] Arun G, N Sasirekha, "Detection of Retinal Hemorrhage in Color Fundus Image using Splat Feature Segmentation", IEEE Sponsored 2nd International Conference on Innovations in Information Embedded and Communication Systems ICIIECS 15, 2015, https://doi.org/10.1109/ICIIECS.2015.7192928
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• [13] Haiying Xia, Fuyu Zhu, "Expanded Mask R-CNN's Retinal Edema Detection Network", ISICDM'19, August 24-26, Xi'an, China. 2019. http://dx.doi.org/10.1145/3364836.3364869
• [14] Guodong Zhang, Hasindu Maduranga Suraweera, Adevian Fairuz Pratama, Dazhe Zhao, Zhaoxuan Gong, and Wei Guo, "Automatic Segmentation of Microaneurysm in Retinal Fundus Images", 2020. http://dx.doi.org/10.1088/1757-899X/768/7/072023
• [15] Ayoub Skouta, Abdelali Elmoufidi, Said Jai, Andaloussi and Ouail Ouchetto, "Hemorrhage semantic segmentation in fundus images for the diagnosis of diabetic retinopathy by using a convolutional neural netrork", Skouta et al. Journal of Big Data network 2022. http://dx.doi.org/10.1186/s40537-022-00632-0

Uwagi

1. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).

2. The authors express their sincere gratitude to the Thiagarajar college of engineering(TCE) for supporting us to carry out this research work. Also the financial support from TCE under Thiagarajar Research Fellowship Scheme.(File no: TRF/July 2021/03).