Image classification is the task of assigning an input image one label from a fixed set of categories. The number of hidden layer nodes in the self-encoder is less than the number of input nodes. That is to say, to obtain a sparse network structure, the activation values of the hidden layer unit nodes must be mostly close to zero. [32] proposed a Sparse Restricted Boltzmann Machine (SRBM) method. Therefore, adding the sparse constraint idea to deep learning is an effective measure to improve the training speed. Deep learning is a class of machine learning algorithms that (pp199–200) uses multiple layers to progressively extract higher-level features from the raw input. So, add a slack variable to formula (12):where y is the actual column vector and r ∈ Rd is the reconstructed residual. Based on the same data selection and data enhancement methods, the original data set is extended to a training set of 498 images and a test set of 86 images. It will build a deep learning model with adaptive approximation capabilities. The essence of deep learning is the transformation of data representation and the dimensionality reduction of data. Its structure is similar to the AlexNet model, but uses more convolutional layers. According to the setting in [53], this paper also obtains the same TCIA-CT database of this DICOM image type, which is used for the experimental test in this section. Even within the same class, its difference is still very large. The classification algorithm proposed in this paper and other mainstream image classification algorithms are, respectively, analyzed on the abovementioned two medical image databases. It is also the most commonly used data set for image classification tasks to be validated and model generalization performance. If the number of hidden nodes is more than the number of input nodes, it can also be automatically coded. Introduction. represents the probability of occurrence of the lth sample x (l). It can reduce dimension information. This also proves the advantages of the deep learning model from the side. Jing et al. The interpretation and understanding of medical images are limited because of different parameters, complexity, and requirement of core subject knowledge. Deep Learning techniques directly identify and extract features, considered by them to be relevant, in a given image dataset. It is a process which involves the following tasks of pre-processing the image (normalization), image segmentation, extraction of key features and identification of the class. It is calculated by sparse representation to obtain the eigendimension of high-dimensional image information. The TCIA-CT database is an open source database for scientific research and educational research purposes. It is applied to image classification, which reduces the image classification Top-5 error rate from 25.8% to 16.4%. In this paper, we explore and compare multiple solutions to the problem of data augmentation in image classification. Based on the study of the deep learning model, combined with the practical problems of image classification, this paper, sparse autoencoders are stacked and a deep learning model based on Sparse Stack Autoencoder (SSAE) is proposed. This section will conduct a classification test on two public medical databases (TCIA-CT database [51] and OASIS-MRI database [52]) and compare them with mainstream image classification algorithms. In the microwave oven image, the appearance of the same model product is the same. Since the training samples are randomly selected, therefore, 10 tests are performed under each training set size, and the average value of the recognition results is taken as the recognition rate of the algorithm under the size of the training set. 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Paper obtains the best classification results are shown in Figure 8,,... Knnrcd algorithm can iteratively optimize the nonnegative sparse representation of kernel functions is proposed to solve formula ( 15.!

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