In order to improve the classification effect of the deep learning model with the classifier, this paper proposes to use the sparse representation classification method of the optimized kernel function to replace the classifier in the deep learning model. Finally, an image classification algorithm based on stacked sparse coding depth learning model-optimized kernel function nonnegative sparse representation is established. Since each hidden layer unit is sparsely constrained in the sparse autoencoder. Similar to unsupervised learning, reinforcement learning algorithms do not rely on labeled data, further they primarily use dynamic programming methods. Comparison table of classification accuracy of different classification algorithms on two medical image databases (unit: %). Instead of assigning the label of the k closest neighbors, you could take an average (mean, µ), weighted averages, etc. Below are mentioned some of the popular algorithms in deep learning: 1. The image classification algorithm is used to conduct experiments and analysis on related examples. In addition, the medical image classification algorithm of the deep learning model is still very stable. Hard SVM classification can also be extended to add or reduce the intercept value. The maximum block size is taken as l = 2 and the rotation expansion factor is 20. h (l) represents the response of the hidden layer. This is because the deep learning model proposed in this paper not only solves the approximation problem of complex functions, but also solves the problem in which the deep learning model has poor classification effect. On the other hand, it has the potential to reduce the sparsity of classes. Then, fine tune the network parameters. (1) Image classification methods based on statistics: it is a method based on the least error, and it is also a popular image statistical model with the Bayesian model  and Markov model [21, 22]. So, if the rotation expansion factor is too large, the algorithm proposed in this paper is not a true sparse representation, and its recognition is not accurate. In the case where the proportion of images selected in the training set is different, there are certain step differences between AlexNet and VGG + FCNet, which also reflects the high requirements of the two models for the training set. The classifier of the nonnegative sparse representation of the optimized kernel function is added to the deep learning model. However, the sparse characteristics of image data are considered in SSAE. This strategy leads to repeated optimization of the zero coefficients. If you go down the neural network path, you will need to use the “heavier” deep learning frameworks such as Google’s TensorFlow, Keras and PyTorch. In view of this, this paper introduces the idea of sparse representation into the architecture of the deep learning network and comprehensively utilizes the sparse representation of good multidimensional data linear decomposition ability and the deep structural advantages of multilayer nonlinear mapping. For example, Zhang et al. Since the calculation of processing large amounts of data is inevitably at the expense of a large amount of computation, selecting the SSAE depth model can effectively solve this problem. It shows that this combined traditional classification method is less effective for medical image classification. If multiple sparse autoencoders form a deep network, it is called a deep network model based on Sparse Stack Autoencoder (SSAE). In order to reflect the performance of the proposed algorithm, this algorithm is compared with other mainstream image classification algorithms. KNN is most commonly using the Euclidean distance to find the closest neighbors of every point, however, pretty much every p value (power) could be used for calculation (depending on your use case). In training, the first SAE is trained first, and the goal of training is to minimize the error between the input signal and the signal reconstructed after sparse decomposition. Wang, P. Tu, C. Wu, L. Chen, and D. Feng, “Multi-image mosaic with SIFT and vision measurement for microscale structures processed by femtosecond laser,”, J. Tran, A. Ufkes, and M. Fiala, “Low-cost 3D scene reconstruction for response robots in real-time,” in, A. Coates, A. Ng, and H. Lee, “An analysis of single-layer networks in unsupervised feature learning,” in, J. VanderPlas and A. Connolly, “Reducing the dimensionality of data: locally linear embedding of sloan galaxy spectra,”, H. Larochelle and Y. Bengio, “Classification using discriminative restricted Boltzmann machines,” in, A. Sankaran, G. Goswami, M. Vatsa, R. Singh, and A. Majumdar, “Class sparsity signature based restricted Boltzmann machine,”, G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,”, A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,”. Classification Algorithms; Regression Algorithms; Classification Algorithms. In 2017, Sankaran et al. If the output is approximately zero, then the neuron is suppressed. It is mainly divided into five steps: first, image preprocessing; second, initialize the network parameters and train the SAE layer by layer; third, a deep learning model based on stacked sparse autoencoder is established; fourth, establish a sparse representation classification of the optimized kernel function; fifth, test the model. The classes are often referred to as target, label or categories. However, a gap in performance has been brought by using neural networks. The process starts with predicting the class of given data points. As an important research component of computer vision analysis and machine learning, image classification is an important theoretical basis and technical support to promote the development of artificial intelligence. Sun, “Faster R-CNN: towards real-time object detection with region proposal networks,”, T. Y. Lin, P. Dollár, R. B. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature pyramid networks for object detection,” in, T. Y. Lin, P. Goyal, and R. Girshick, “Focal loss for dense object detection,” in, G. Chéron, I. Laptev, and C. Schmid, “P-CNN: pose-based CNN features for action recognition,” in, C. Feichtenhofer, A. Pinz, and A. Zisserman, “Convolutional two-stream network fusion for video action recognition,” in, H. Nam and B. Han, “Learning multi-domain convolutional neural networks for visual tracking,” in, L. Wang, W. Ouyang, and X. Wang, “STCT: sequentially training convolutional networks for visual tracking,” in, R. Sanchez-Matilla, F. Poiesi, and A. Cavallaro, “Online multi-target tracking with strong and weak detections,”, K. Kang, H. Li, J. Yan et al., “T-CNN: tubelets with convolutional neural networks for object detection from videos,”, L. Yang, P. Luo, and C. Change Loy, “A large-scale car dataset for fine-grained categorization and verification,” in, R. F. Nogueira, R. de Alencar Lotufo, and R. Campos Machado, “Fingerprint liveness detection using convolutional neural networks,”, C. Yuan, X. Li, and Q. M. J. Wu, “Fingerprint liveness detection from different fingerprint materials using convolutional neural network and principal component analysis,”, J. Ding, B. Chen, and H. Liu, “Convolutional neural network with data augmentation for SAR target recognition,”, A. Esteva, B. Kuprel, R. A. Novoa et al., “Dermatologist-level classification of skin cancer with deep neural networks,”, F. A. Spanhol, L. S. Oliveira, C. Petitjean, and L. Heutte, “A dataset for breast cancer histopathological image classification,”, S. Sanjay-Gopal and T. J. Hebert, “Bayesian pixel classification using spatially variant finite mixtures and the generalized EM algorithm,”, L. Sun, Z. Wu, J. Liu, L. Xiao, and Z. Wei, “Supervised spectral-spatial hyperspectral image classification with weighted Markov random fields,”, G. Moser and S. B. Serpico, “Combining support vector machines and Markov random fields in an integrated framework for contextual image classification,”, D. G. Lowe, “Object recognition from local scale-invariant features,” in, D. G. Lowe, “Distinctive image features from scale-invariant keypoints,”, P. Loncomilla, J. Ruiz-del-Solar, and L. Martínez, “Object recognition using local invariant features for robotic applications: a survey,”, F.-B. The stack sparse autoencoder is a constraint that adds sparse penalty terms to the cost function of AE. 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