Retinal Medical Image Classification Based on Deep Convolutional Neural Network AlexNet

Eye diseases will have a very serious impact on the life, study and work of patients. In order to better assist doctors in their work, it is very meaningful to use deep learning neural networks for medical image analysis and auxiliary medical diagnosis. In this paper, we use deep neural network AlexNet combined with Adam optimization algorithm to classify images of four common eye diseases: vitreous opacity, vitreous opacity with retinal detachment, asteroid hyalosis and vitreous hemorrhage. Use confusion matrix, accuracy, precision, recall, specificity and other evaluation indicators to evaluate its classification effect. The application results of the above methods on ophthalmic ultrasound images from actual hospitals show that AlexNet has high classification accuracy for actual ultrasound pattern, and can be used to assist doctors in ophthalmic disease diagnosis.