An intelligent strabismus detection method based on convolution neural network

Strabismus is one of the widespread vision disorders in which eyes are misaligned and asymmetric. Convolutional neural networks (CNNs) properly designed for analyzing images detecting texture patterns. In this paper, we proposed a system that uses deep learning CNN applications automatically classifying strabismus disorder. The includes two main stages: first, detection facial eye segmentation using viola-jones algorithm. second stage to map segmented area according iris position each eye. This method applied three datasets, gathered as digital images. section covers region. Besides, evaluation equations measuring performance. has undergone numerous experiments various stages simulate analyze performance layers through different classifiers variant thresholds ratio. researchers investigated experimental outcomes during training testing phases obtained promising results exhibit effectiveness system. According results, accuracy technique reached 95.62%.