Section 2 of this paper will mainly explain the deep learning model based on stack sparse coding proposed in this paper. Skin lesion classification from dermoscopic images using deep learning techniques Abstract: The recent emergence of deep learning methods for medical image analysis has enabled the development of intelligent medical imaging-based diagnosis systems that can assist the human expert in making better decisions about a patients health. 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,”. However, this type of method still cannot perform adaptive classification based on information features. Based on steps (1)–(4), an image classification algorithm based on stacked sparse coding depth learning model-optimized kernel function nonnegative sparse representation is established. h (l) represents the response of the hidden layer. This results in low performance compared to deep learning-based algorithms. The interpretation and understanding of medical images are limited because of different parameters, complexity, and requirement of core subject knowledge. Deep learning is mostly for highly nonlinear and large-size classification problems. The Automatic Encoder Deep Learning Network (AEDLN) is composed of multiple automatic encoders. This is one of the core problems in Computer Vision that, despite its simplicity, has a large variety of practical applications. These techniques can diagnose many diseases and injuries like cancer, pneumonia, brain injuries, internal bleeding, and so on. 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. In this paper, the image in the ImageNet data set is preprocessed before the start of the experimental process, with a uniform size of 256 × 256. The class to be classified is projected as , and the dictionary is projected as . Find out if you're eligible for Springboard's Machine Learning Career Track. Basic schematic diagram of the stacked sparse autoencoder. In deep learning, the more sparse self-encoding layers, the more characteristic expressions it learns through network learning and are more in line with the data structure characteristics. SSAE’s model generalization ability and classification accuracy are better than other models. The particle loss value required by the NH algorithm is li,t = r1. Let denote the target dictionary and denote the background dictionary, then D = [D1, D2]. For example, in the coin image, although the texture is similar, the texture combination and the grain direction of each image are different. These traditional machine learning algorithms rely heavily on carefully crafted features by subject matter experts, which is a demanding process, Medical images vary among patients, and feature generation also differs among subject matter experts. m represents the number of training samples. The image classification algorithm is used to conduct experiments and analysis on related examples. The maximum block size is taken as l = 2 and the rotation expansion factor is 20. However, empirical results for the image data set have shown that the texture descriptor method proposed, regardless of the strategy employed is very competitive when compared with Convolutional Neural Network for all the performed experiments. [32] proposed a Sparse Restricted Boltzmann Machine (SRBM) method. Chest X-ray is the first imaging technique that plays an important role in the diagnosis of COVID-19 disease. (3) Image classification method based on shallow learning: in 1986, Smolensky [28] proposed the Restricted Boltzmann Machine (RBM), which is widely used in feature extraction [29], feature selection [30], and image classification [31]. Because although this method is also a variant of the deep learning model, the deep learning model proposed in this paper has solved the problems of model parameter initialization and classifier optimization. INTRODUCTION Recently, image classification is growing and becoming a trend among technology … This paper chooses to use KL scatter (Kullback Leibler, KL) as the penalty constraint:where s2 is the number of hidden layer neurons in the sparse autoencoder network, such as the method using KL divergence constraint, then formula (4) can also be expressed as follows: When , , if the value of differs greatly from the value of ρ, then the term will also become larger. Thanks to transfer learning, an effective mechanism that can provide a promising solution by transferring knowledge from generic object recognition tasks to domain-specific tasks. Basic flow chart of image classification algorithm based on stack sparse coding depth learning-optimized kernel function nonnegative sparse representation. In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of deep neural networks, most commonly applied to analyzing visual imagery. This blog post is part two in our three-part series of building a Not Santa deep learning classifier (i.e., a deep learning model that can recognize if Santa Claus is in an image or not): 1. The data used to support the findings of this study are included within the paper. Image classification began in the late 1950s and has been widely used in various engineering fields, human-car tracking, fingerprints, geology, resources, climate detection, disaster monitoring, medical testing, agricultural automation, communications, military, and other fields [14–19]. In visual field, the records of image classification have been broken in the ImageNet Challenge 2012 by using deep convolutional neural network (CNN) [1]. At the same time, combined with the practical problem of image classification, this paper proposes a deep learning model based on the stacked sparse autoencoder. The classification accuracies of the VGG-19 model will be visualized using the … Therefore, if you want to achieve data classification, you must also add a classifier to the last layer of the network. Copyright © 2020 Jun-e Liu and Feng-Ping An. These benefits over traditional approaches lead to their fast adaptation in medical imaging, as mentioned in the next section. The weights obtained by each layer individually training are used as the weight initialization values of the entire deep network. Comparison table of classification accuracy of different classification algorithms on two medical image databases (unit: %). Introduction. This is also the main reason why the deep learning image classification algorithm is higher than the traditional image classification method. The SSAE depth model directly models the hidden layer response of the network by adding sparse constraints to the deep network. Example picture of the OASIS-MRI database. So, the gradient of the objective function H (C) is consistent with Lipschitz’s continuum. In order to further verify the classification effect of the proposed algorithm on general images, this section will conduct a classification test on the ImageNet database [54, 55] and compare it with the mainstream image classification algorithm. It can improve the image classification effect. Therefore, can be used to represent the activation value of the input vector x for the first hidden layer unit j, then the average activation value of j is. (2)Because deep learning uses automatic learning to obtain the feature information of the object measured by the image, but as the amount of calculated data increases, the required training accuracy is higher, and then its training speed will be slower. It is assumed that the training sample set of the image classification is , and is the image to be trained. In the ideal case, only one coefficient in the coefficient vector is not 0. Previous work has demonstrated the … M. Z. Alom, T. M. Taha, and C. Yakopcic, “The history began from AlexNet: a comprehensive survey on deep learning approaches,” 2018, R. Cheng, J. Zhang, and P. Yang, “CNet: context-aware network for semantic segmentation,” in, K. Clark, B. Vendt, K. Smith et al., “The cancer imaging archive (TCIA): maintaining and operating a public information repository,”, D. S. Marcus, T. H. Wang, J. Parker, J. G. Csernansky, J. C. Morris, and R. L. Buckner, “Open access series of imaging studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults,”, S. R. Dubey, S. K. Singh, and R. K. Singh, “Local wavelet pattern: a new feature descriptor for image retrieval in medical CT databases,”, J. Deng, W. Dong, and R. Socher, “Imagenet: a large-scale hierarchical image database,” in. It is calculated by sparse representation to obtain the eigendimension of high-dimensional image information. This function will calculate the covariance matrix as we have seen above. It will build a deep learning model with adaptive approximation capabilities. In this article, we’ll discuss medical imaging and the evolution of deep learning-based techniques. The Effectiveness of Data Augmentation in Image Classification using Deep Learning. The statistical results are shown in Table 3. The Effectiveness of Data Augmentation in Image Classification using Deep Learning Jason Wang Stanford University 450 Serra Mall [email protected] Luis Perez Google 1600 Amphitheatre Parkway [email protected] Abstract In this paper, we explore and compare multiple solutions to the problem of data augmentation in image classification. In CNNs, the nodes in the hidden layers don’t always share their output with every node in the next layer (known as convolutional layers). The best … Jing, F. Wu, Z. Li, R. Hu, and D. Zhang, “Multi-label dictionary learning for image annotation,”, Z. Zhang, W. Jiang, F. Li, M. Zhao, B. Li, and L. Zhang, “Structured latent label consistent dictionary learning for salient machine faults representation-based robust classification,”, W. Sun, S. Shao, R. Zhao, R. Yan, X. Zhang, and X. Chen, “A sparse auto-encoder-based deep neural network approach for induction motor faults classification,”, X. Han, Y. Zhong, B. Zhao, and L. Zhang, “Scene classification based on a hierarchical convolutional sparse auto-encoder for high spatial resolution imagery,”, A. Karpathy and L. Fei-Fei, “Deep visual-semantic alignments for generating image descriptions,” in, T. Xiao, H. Li, and W. Ouyang, “Learning deep feature representations with domain guided dropout for person re-identification,” in, F. Yan, W. Mei, and Z. Chunqin, “SAR image target recognition based on Hu invariant moments and SVM,” in, Y. Nesterov, “Efficiency of coordinate descent methods on huge-scale optimization problems,”. It is also capable of capturing more abstract features of image data representation. The results of the other two comparison depth models DeepNet1 and DeepNet3 are still very good. Randomly select 20%, 30%, 40%, and 70% of the original data set as the training set and the rest as the test set. The overall cost function can be expressed as follows: Among them, the coefficient β is a sparse penalty term, the value of related to W, b, and H (W, b) is a loss function, which can be expressed as follows: The abovementioned formula gives the overall cost function, and the residual or loss of each hidden layer node is the most critical to construct a deep learning model based on stacked sparse coding. Its structure is similar to the AlexNet model, but uses more convolutional layers. In this project, we will build a convolution neural network in Keras with python on a CIFAR-10 dataset. I suggest you you to use AdaBoost or SVM classifier for this kind of simple classification tasks. 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. [41] proposed a valid implicit label consistency dictionary learning model to classify mechanical faults. It can be seen from Figure 7, it is derived from an example in each category of the database. After that, many architectures came that include VGG Net, Inception (GoogleNet), ResNet, etc. For example, Zhang et al. The SSAE is implemented by the superposition of multiple sparse autoencoders, and the SSAE is the same as the deep learning model. At the same time, as shown in Table 2, when the training set ratio is very low (such as 20%), the recognition rate can be increased by increasing the rotation expansion factor. Section 5 analyzes the image classification algorithm proposed in this paper and compares it with the mainstream image classification algorithm. This questions the reliability of this traditional approach, Traditional machine learning algorithms process raw image data without taking hidden and subtle representations into account. If the number of hidden nodes is more than the number of input nodes, it can also be automatically coded. Deep learning allows machines to identify and extract features from images. Krizhevsky et al. During the training process, the output reconstruction signal of each layer is used to compare with the input signal to minimize the error. The experimental results are shown in Table 1. All the pictures are processed into a gray scale image of 128 × 128 pixels, as shown in Figure 5. It can efficiently learn more meaningful expressions. It can be seen from Table 2 that the recognition rate of the proposed algorithm is high under various rotation expansion multiples and various training set sizes. This section uses Caltech 256 [45], 15-scene identification data set [45, 46], and Stanford behavioral identification data set [46] for testing experiments. represents the expected value of the jth hidden layer unit response. We will be providing unlimited waivers of publication charges for accepted research articles as well as case reports and case series related to COVID-19. (4)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. At the same time, the performance of this method in both medical image databases is relatively stable, and the classification results are also very accurate. The present classification methods for remote-sensing images are grouped according to the features they use into: manual feature-based methods, unsupervised feature learning methods, and supervised feature learning methods. 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. SSAE training is based on layer-by-layer training from the ground up. 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