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Advanced diabetic retinopathy detection with the R-CNN: A unified visual health solution

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Sri Sravya V. , Naga Srinivasu P. , Shafi J. , Hołubowski W. , Zielonka A.

tom Vol. 34, no. 4

661--678

Diabetic retinopathy (DR) can cause blindness and vision impairment. This degenerative eye condition may lead to an irreversible vision loss. The prevalence of vision impairment and blindness caused by DR emphasizes the critical need for better screening and therapy measures. DR aetiology involves persistent hyperglycemia-induced microvascular abnormalities, oxidative stress, inflammatory reactions, and retinal blood flow changes. Common screening methods for retinal issues include fundus photography, OCT, and fluorescein angiography. For those with diabetic macular edema (DME), it is a common cause of vision loss. Our goal is to develop an automated, cost-effective method for identifying diabetic retinal disease specimens. This study introduces a faster R-CNN method for detecting and classifying DR lesions in retinal images. Those are classified across five different classes. An extensive analysis of 88,704 images from a Kaggle dataset indicates the efficiency of the proposed model, with a reasonable accuracy of 98.38%. The proposed method is robust in disease localization and classification tasks and it has outperformed other existing studies in DR recognition. On evaluating cross-datasets in Kaggle and APTOS, the model has yield better results during training and testing phases.

A novel deep learning-based approach for prediction of neonatal respiratory disorders from chest X-ray images

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Erdogan Yildirim A. , Canayaz M.

tom Vol. 43, no. 4

635--655

In recent years, many diseases can be diagnosed in a short time with the use of deep learning models in the field of medicine. Most of the studies in this area focus on adult or pediatric patients. However, deep learning studies for the diagnosis of diseases in neonatal are not sufficient. Also, since it is known that respiratory disorders such as pneumonia have a large place among the causes of neonatal death, early and accurate diagnosis of respiratory diseases in neonates is crucial. For this reason, our study aims to detect the presence of respiratory disorders through the developed deep-learning approach using chest X-ray images of patients hospitalized in the Neonatal Intensive Care Unit. Accordingly, the enhanced version of C+EffxNet, the new hybrid deep learning model, is designed to predict respiratory disorders in neonates. In this version, the features selected by PCA are combined as 100, 200, and 300, then the binary classification process was carried out. In the study, the accuracy and kappa value were obtained as 0.965, and 0.904, respectively before feature merging, while these values were obtained as 0.977, and 0.935 after feature merging. This method, which was developed for the diagnosis of respiratory disorders in neonates, was also subsequently applied to a chest X-ray dataset that is frequently used in the literature for the diagnosis of pediatric pneumonia. For this data set, while the accuracy was 0.992, the kappa value was 0.982. The results obtained confirm the success of the proposed method for both datasets.