Wyniki wyszukiwania - BazTech

Ograniczanie wyników

1 Acta Mechanica et Automatica

1 2024

Powiadomienia systemowe

Sesja wygasła!
Sesja wygasła!
Sesja wygasła!
Sesja wygasła!
Sesja wygasła!

Znaleziono wyników: 1

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: Drusen

Sortuj według:

Ogranicz wyniki do:

Automatic method of macular diseases detection using deep cnn-gru network in oct images

Powroznik Paweł, Skublewska-Paszkowska Maria, Rejdak Robert, Nowomiejska Katarzyna

Acta Mechanica et Automatica

2024

Vol. 18, no. 4

697--706

The increasing development of Deep Learning mechanism allowed ones to create semi-fully or fully automated diagnosis software solutions for medical imaging diagnosis. The convolutional neural networks are widely applied for central retinal diseases classifi-cation based on OCT images. The main aim of this study is to propose a new network, Deep CNN-GRU for classification of early-stage and end-stages macular diseases as age-related macular degeneration and diabetic macular edema (DME). Three types of disorders have been taken into consideration: drusen, choroidal neovascularization (CNV), DME, alongside with normal cases. The created automatic tool was verified on the well-known Labelled Optical Coherence Tomography (OCT) dataset. For the classifier evaluation the following measures were calculated: accuracy, precision, recall, and F1 score. Based on these values, it can be stated that the use of a GRU layer directly connected to a convolutional network plays a pivotal role in improving previously achieved results. Additionally, the proposed tool was compared with the state-of-the-art of deep learning studies performed on the Labelled OCT dataset. The Deep CNN-GRU network achieved high performance, reaching up to 98.90% accuracy. The obtained results of classification performance place the tool as one of the top solutions for diagnosing retinal diseases, both early and late stage.