- Tytuł:
- Automatic method of macular diseases detection using deep cnn-gru network in oct images
- Autorzy:
-
Powroznik, Paweł
Skublewska-Paszkowska, Maria
Rejdak, Robert
Nowomiejska, Katarzyna - Tematy:
-
Drusen
Deep CNN-GRU
AMD classification
OCT
deep learning - Pokaż więcej
- Wydawca:
- Politechnika Białostocka. Oficyna Wydawnicza Politechniki Białostockiej
- Powiązania:
- https://bibliotekanauki.pl/articles/58907191.pdf Link otwiera się w nowym oknie
- Opis:
- 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.
- Dostawca treści:
- Biblioteka Nauki
Artykuł