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2 2024

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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.

Application of convolutional gated recurrent units u-net for distinguishing between retinitis pigmentosa and cone–rod dystrophy

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

Acta Mechanica et Automatica

2024

Vol. 18, no. 3

513--505

Artificial Intelligence (AI) has gained a prominent role in the medical industry. The rapid development of the computer science field has caused AI to become a meaningful part of modern healthcare. Image-based analysis involving neural networks is a very important part of eye diagnoses. In this study, a new approach using Convolutional Gated Recurrent Units (GRU) U-Net was proposed for the classifying healthy cases and cases with retinitis pigmentosa (RP) and cone–rod dystrophy (CORD). The basis for the classification was the location of pigmentary changes within the retina and fundus autofluorescence (FAF) pattern, as the posterior pole or the periphery of the retina may be affected. The dataset, gathered in the Chair and Department of General and Pediatric Ophthalmology of Medical University in Lublin, consisted of 230 ultra-widefield pseudocolour (UWFP) and ultra-widefield FAF images, obtained using the Optos 200TX device (Optos PLC). The data were divided into three categories: healthy subjects (50 images), patients with CORD (48 images) and patients with RP (132 images). For applying deep learning classification, which rely on a large amount of data, the dataset was artificially enlarged using augmentation involving image manipulations. The final dataset contained 744 images. The proposed Convolutional GRU U-Net network was evaluated taking account of the following measures: accuracy, precision, sensitivity, specificity and F1. The proposed tool achieved high accuracy in a range of 91.00%–97.90%. The developed solution has a great potential in RP diagnoses as a supporting tool.