With the growth of the aging population, the incidence of eye diseases is getting higher and higher. Traditional manual diagnosis has strong subjectivity and limitations. Computer-aided diagnosis can improve the accuracy of diagnosis while accelerating the diagnosis. The traditional convolutional neural network cannot fully obtain the effective features of the image, which makes the classification accuracy of the image low. The computer-aided diagnosis algorithm proposed in this paper integrates DenseNet and Squeeze-and-Excitation Networks (SENet) in deep learning based on image de-watermarking and data enhancement, while fully extracting and utilizing fundus images features while improving the network's global features information utilization. The experimental results show that the classification accuracy of the model in the fundus image is 0.9528. Compared with other convolutional networks, SEDenseNet achieves the highest accuracy.
Skin diseases not only endanger physical health but also cause psychological problems. Traditional manual diagnosis has strong subjectivity and limitations. Recently, the use of computer-aided diagnosis technology based on deep convolutional neural networks to classify and recognize dermatological images has been widely used. In order to further improve the classification effect, we propose a method to merge the SENet network with the Inception-v4 network. By comparing the DensenNet-121, VGG-16, and ResNet-101 networks, the effectiveness of the SE-Inception-v4 network is verified, and the SENet network has also verified the effectiveness of model performance improvement. Experimental results show that the improved deep learning algorithm in this paper can improve the accuracy of skin disease image classification and has certain guiding significance for the research and application of computer-aided diagnosis in the medical field.
Based on the pictures of rural housing buildings, the characteristics of housing for the poor are studied, and the appearance of wall is classified by the deep learning method. The degree of poverty is determined by the classification of wall characteristics. Using the transfer learning method, the ResNet101 network is combined with the AdaptNet network to train the house image set. The house pictures are classified using the trained model. Experiments show that the classification accuracy in the recognition of wooden walls and tile walls is improved.
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