Analysis of Convolutional Neural Network based Image Classification Techniques

Tripathi, Milan

doi:10.36548/jiip.2021.2.003

Cited by 226 publications

(38 citation statements)

References 24 publications

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“…e convolutional layer requires an activation function for the convolution operation; that is, when the convolutional layer and the fully connected layer perform transformation operations on the input, the activation function and the weights w and bias b of the neurons are used, while the pooling layer performs a fixed function operation [15]. e parameters in the convolutional and fully connected layers are trained with gradient descent so that the classification score computed by the CNN matches the label of each image given in the training set.…”

Section: Cnn Technology Eorymentioning

confidence: 99%

Credit Risk Evaluation in Enterprise Financial Management by Using Convolutional Neural Network under the Construction of Smart City

Hui

2022

Security and Communication Networks

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Smart cities are the forward goals of future cities, and the development of small- and medium-sized enterprises (SMEs) plays a key role in it. To solve the financing difficulties of SMEs, it is necessary to scientifically, objectively, and accurately assess the credit risk of SMEs. Based on the current research and analysis of scholars in related fields, taking corporate financial data as the object, the SMEs’ credit risk is assessed through an adaptive and self-learning convolutional neural network (CNN) method. The GoogleNet method is further improved to reconstruct the credit risk evaluation model of SMEs. Finally, the influence of the convolution kernel depth on the accuracy is discussed through the optimization of the structure and the selection of the initial value of the learning rate. The results are verified by using the data in the 2016–2021 SME sector in Shanghai and Shenzhen in the wind database. It can be found that when the learning rate is finally set to 0.4 and the convolution kernel depth is set to 8 and 32 in turn, the evaluation accuracy of the test dataset is the highest, with a total accuracy of 96.8%. Therefore, it is believed that the newly constructed model has higher accuracy and more practical value than the other three typical risk assessment models.

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Section: Cnn Technology Eorymentioning

confidence: 99%

Credit Risk Evaluation in Enterprise Financial Management by Using Convolutional Neural Network under the Construction of Smart City

Hui

2022

Security and Communication Networks

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“…The multifocused picture division is a class of picture handling between the bottom-level picture handling and the middle-level analysis of pictures. Picture division is an important element in analysis of pictures, which is the process of dividing the original image into several subregions that are not connected to each other and extracting the part of interest from these regions [8]. Traditional picture division methods are mainly based on the underlying image features that can be observed by the human eye, such as color, texture, and edges [9].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Analysis of Multifocus Picture Division Algorithm Based on Deep Learning

You¹

2022

Journal of Function Spaces

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As a complex machine learning algorithm, deep learning can extract object shape information and more complex and advanced information in images by using a deep learning model. In order to solve some problems of deep learning in image feature extraction and classification, this paper designs a modeling method of multifocus image segmentation algorithm based on deep learning. The acceleration effect of FPGA (field programmable gate array) on deep learning and weight sharing is analyzed. By introducing deep learning, the trouble of determining the weight coefficient is eliminated, and the energy function is simplified. Therefore, the relevant parameters of multifocus image segmentation can be easily set, and better results can be obtained. The multifocus image segmentation algorithm based on deep learning can not only obtain closed and smooth segmentation curves but also adaptively deal with topology changes due to high segmentation accuracy and stable algorithm. The results show that the model effectively combines the local and global information of the image, so that the model has good robustness. The depth learning algorithm is used to calculate the average value of local inner and outer pixels of an image. Even for complex images, relatively simple contour curves can be obtained.

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“…The architecture of an example VGG16 CNN model consisting of five convolutional layers and the fc6, fc7, fc8 and softmax layers are presented in Figure 4 [19,20]. The first layers learn the edge and color information through the convolution layers of CNN and the kernels within the remaining layers learn the information about the image details [21]. The mathematical expression of the convolution is given in Eq.…”

Section: Convolution Neural Networkmentioning

confidence: 99%

Classification of Tympanic Membrane Images based on VGG16 Model

Çalışkan

2022

Kocaeli Journal of Science and Engineering

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Otitis Media (OM) is a type of infectious disease caused by viruses and/or bacteria in the middle ear cavity. In the current study, it was aimed to detect the eardrum region in middle ear images and thus to diagnose OM disease by using artificial intelligence methods. The Convolution Neural Networks (CNN) model and the deep features of this model and the images obtained with the otoscope device were used. In order to separate these images as Normal and Anomalous, the end-to-end VGG16 model was directly used in the first stage of the experimental work. In the second stage of the experimental study, the activation maps of the fc6 and fc7 layers consisting of 4096 features and the Fc8 layer consisting of 1000 features of the VGG16 CNN model were obtained. Then, it was given as input to Support Vector Machines (SVM). Then, the deep features obtained from all activation maps were combined and a new feature set was obtained. In the last stage, this feature set is given as an input to SVM. Thus, the effect of the VGG16 model and the features obtained from the layers of this model on the success of distinguishing images of the eardrum was investigated. As a result of the experimental study, the best performance results were obtained with the fc6 layer. The results showed that OM disease could be accurately detected by using a deep CNN architecture. The proposed deep learning-based classification system promises highly accurate results for disease detection.

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Analysis of Convolutional Neural Network based Image Classification Techniques

Cited by 226 publications

References 24 publications

Credit Risk Evaluation in Enterprise Financial Management by Using Convolutional Neural Network under the Construction of Smart City

Credit Risk Evaluation in Enterprise Financial Management by Using Convolutional Neural Network under the Construction of Smart City

Modeling and Analysis of Multifocus Picture Division Algorithm Based on Deep Learning

Classification of Tympanic Membrane Images based on VGG16 Model

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