Early Detection of Retinopathy of Prematurity Using Machine Learning (Convolutional Neural Network)
DOI:
https://doi.org/10.48314/ceti.v1i2.27Keywords:
Retinopathy, Convolutional neural networks, Kaggle, Machine learningAbstract
This paper describes a method for using an Machine Learning (ML) model to detect Retinopathy of Prematurity (RoP) disease in newborn infants. To diagnose the disease, a suitable ML algorithm should be used. The Convolutional Neural Network (CNN) method is used in this situation because it can effectively extract the smallest information from images. 16000 images are used in this collection. The anatomy and presence of a demarcation line in the retina can be used to distinguish between early ROP stages. To accomplish this, our proposed technique first trains an object segmentation model to recognize the demarcation line at the pixel level, and then adds the resulting mask as an additional "color" channel in the original image. The method then trains a CNN classifier on the changed images using data from both the original image and the mask, helping to focus the model's attention on the demarcation line. An accuracy of 85% has been observed in this model.
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