Employing deep learning architectures for image-based automatic cataract diagnosis
Yükleniyor...
Tarih
2021
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
TÜBİTAK
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Various eye diseases affect the quality of human life severely and ultimately may result in complete vision loss. Ocular diseases manifest themselves through mostly visual indicators in the early or mature stages of the disease by showing abnormalities in optics disc, fovea, or other descriptive anatomical structures of the eye. Cataract is among the most harmful diseases that affects millions of people and the leading cause of public vision impairment. It shows major visual symptoms that can be employed for early detection before the hypermature stage. Automatic diagnosis systems intend to assist ophthalmological experts by mitigating the burden of manual clinical decisions and on health care utilization. In this study, a diagnosis system based on color fundus images are addressed for cataract disease. Deep learning-based models were performed for the automatic identification of cataract diseases. Two pretrained robust architectures, namely VGGNet and DenseNet, were employed to detect abnormalities in descriptive parts of the human eye. The proposed system is implemented on a wide and unique dataset that includes diverse color retinal fundus images that are acquired comparatively in low-cost and common modality, which is considered a major contribution of the study. The dataset show symptoms of cataracts in different phases and represents the characteristics of the cataract. By the proposed system, dysfunction associated with cataracts could be identified in the early stage. The achievement of the proposed system is compared to various traditional and up-to-date classification systems. The proposed system achieves 97.94% diagnosis rate for cataract disease grading.
Açıklama
Anahtar Kelimeler
Deep learning, textural features, automatic diagnosis, cataract
Kaynak
Turkish Journal of Electrical Engineering & Computer Sciences
WoS Q Değeri
Q4
Scopus Q Değeri
N/A
Cilt
29
Sayı
sı-1
Künye
Acar, E., Türk, Ö., Ertuğrul, Ö. F., Aldemir, E. (2021).CEmploying deep learning architectures for image-based automatic cataract diagnosis. Turkish Journal of Electrical Engineering & Computer Sciences. 29(sı-1), s. 2649-2662
