Retinopathy of prematurity (ROP) can cause blindness in premature neonates. It is diagnosed when new blood vessels form abnormally in the retina. However, people at high risk of ROP might benefit significantly from early detection and treatment. Therefore, early diagnosis of ROP is vital in averting visual impairment. However, due to a lack of medical experience in detecting this condition, many people refuse treatment; this is especially troublesome given the rising cases of ROP. To deal with this problem, we trained three transfer learning models (VGG-19, ResNet-50, and EfficientNetB5) and a convolutional neural network (CNN) to identify the zones of ROP in preterm newborns. The dataset to train the model contains 1365 fundus images from the ROP screening. This dataset was gathered from the Private Clinic Al-Amal Eye center in Baghdad, Iraq. The models above are ensemble through voting classifier techniques to increase the performance. The proposed method had an overall accuracy of 88.82 percent when employing the voting classifier. On the other hand, EfficientNetB5 has outperformed other models in terms of accuracy with 87.27%.

اعتلال الشبكية الخداجي (ROP) هو السبب الأكثر شيوعًا لعمى الأطفال الذي لا رجعة فيه، ويعتمد تشخيصه وعلاجه على الدرجات الذاتية بناءً على سمات الأوعية الدموية في شبكية العين. ومع ذلك، فإن هذه الطريقة شاقة وعرضة للخطأ، لذا فإن الأساليب الآلية مرغوبة لمزيد من الدقة والإنتاجية. تهدف هذه الدراسة إلى تطوير نهج قائم على التعلم العميق للتشخيص الدقيق لمرض اعتلال الشبكية الخداجي الزائد عند الأطفال الخدج باستخدام نماذج التعلم التحويلية وتقنية تصنيف الاندماج. زودنا مركز الأمل للعيون في العيادة الخاصة في بغداد، العراق، بـ 2776 صورة لقاعدة فحص مرضى اعتلال الشبكية الخداجي بين عامي 2015 و2020، واستخدمنا هذه الصور لتدريب ثلاثة نماذج للشبكات العصبية التلافيفية العميقة (ResNet50 وDensenet161 وEfficientNetB5). تم استخدام نهج مصنف الاندماج لدمج النماذج الثلاثة من أجل تشخيص شامل ودقيق. تتميز النماذج الثلاثة بمعدلات دقة نسبية تبلغ 69.78٪ و80.57٪ و81.29٪ في تصنيفاتها الخاصة. ومع ذلك، زادت الدقة الإجمالية إلى 90.28 في المائة عند استخدام مصنف الاندماج. هذا يدل على أن الطريقة المقترحة مفيدة لتحديد اعتلال الشبكية الخداجي عند الخدج. تشير نتائج الدراسة إلى أن الطريقة المقترحة لديها...

A significant cause of blindness in preterm infants is retinal retinopathy of prematurity (ROP). Early detection and intervention are essential for preventing visual loss. This study proposes an advanced approach for predicting ROP stages using deep learning algorithms and belief function theory. Three steps comprise the suggested technique: image pre-processing, feature extraction using deep learning models, and classification utilizing belief function theory. We used a dataset of 3720 retinal images from premature infants and achieved a classification accuracy of 95.57% for predicting ROP stages. Our results demonstrate the effectiveness of deep learning algorithms and belief function theory in ROP diagnosis. This strategy can increase the efficacy and precision of ROP diagnosis, improving the treatment course for premature infants at risk of vision loss.

Retinopathy of prematurity (ROP) is one of the notable causes of vision impairment among kids. The retinal zone, among the signs of ROP, is clinically considered a better predictor of severe forms than staging. This study explores five convolutional neural network (CNN) models used for computerized ROP zone classification based on fundus images of the retina. A total database of 1,365 images drawn from Al Amal Eye Center, located in Baghdad-Iraq, was trained into three classes mimicking three varied ROP zones. The images were used to finetune the models on top of pre-trained VGG16, VGG19, Xception, Inception-ResNetV2, and Inception-V3 models, whose network sizes and configurations were varied among the five models. Compared to minimal works done, the Inception-V3 model yielded the highest accuracy and reached 94.04% on zone detection. Computerized detection of the retinal lesions among pre-terms is fundamental to guiding the regimen plan that may incorporate laser therapy, intravitreal implantation, or close observation with intervention on demand. Merging computerized interpretation with the expertise of pediatric ophthalmologists may guide more consistent decisions on the management plan of the ROP. Future work will include external validation on independent multi-center datasets to assess generalizability.