A novel approach to reconstruction based saliency detection via convolutional neural network stacked with auto-encoder

Abstract. Traditional methods for detecting maize leaf diseases (such as leaf blight, sooty blotch, brown spot, rust, and purple leaf sheaf) are typically labor-intensive and strongly subjective. With the aim of achieving high accuracy and efficiency in the identification of maize leaf diseases from digital imagery, this article proposes a novel multichannel convolutional neural network (MCNN). The MCNN is composed of an input layer, five convolutional layers, three subsampling layers, three fully connected layers, and an output layer. Using a method that imitates human visual behavior in video saliency detection, the first and second subsampling layers are connected directly with the first fully connected layer. In addition, the mixed modes of pooling and normalization methods, rectified linear units (ReLU), and dropout are introduced to prevent overfitting and gradient diffusion. The learning process corresponding to the network structure is also illustrated. At present, there are no large-scale images of maize leaf disease for use as experimental samples. To test the proposed MCNN, 10,820 RGB images containing five types of disease were collected from maize planting areas in Shandong Province, China. The original images could not be used directly in identification experiments because of noise and irrelevant regions. They were therefore denoised and segmented by homomorphic filtering and region of interest (ROI) segmentation to construct a standard database. A series of experiments on 8 GB graphics processing units (GPUs) showed that the MCNN could achieve an average accuracy of 92.31% and a high efficiency in the identification of maize leaf diseases. The multichannel design and the integration of different innovations proved to be helpful methods for boosting performance. Keywords: Artificial intelligence, Convolutional neural network, Deep learning, Image classification, Machine learning algorithms, Maize leaf disease.

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A Novel Approach for Identification of Brain Tumor by Combination of Intelligent Water Drop Algorithm and Convolutional Neural Network

10.1007/978-981-16-3915-9_19 ◽

2021 ◽

pp. 237-245

Author(s):

Ashish Kumar Dehariya ◽

Pragya Shukla

Keyword(s):

Neural Network ◽

Brain Tumor ◽

Convolutional Neural Network ◽

Water Drop ◽

Novel Approach

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Security of E-Health Systems Using Face Recognition Based on Convolutional Neural Network

International Journal of Extreme Automation and Connectivity in Healthcare ◽

10.4018/ijeach.2020070104 ◽

2020 ◽

Vol 2 (2) ◽

pp. 37-41

Author(s):

Zhixian Chen ◽

Jialin Tang ◽

Xueyuan Gong ◽

Qinglang Su

Keyword(s):

Neural Network ◽

Face Recognition ◽

Convolutional Neural Network ◽

Principal Component ◽

Activation Function ◽

Support Vector ◽

Data Set ◽

Novel Approach ◽

Rectified Linear Unit ◽

The Face

In order to improve the low accuracy of the face recognition methods in the case of e-health, this paper proposed a novel face recognition approach, which is based on convolutional neural network (CNN). In detail, through resolving the convolutional kernel, rectified linear unit (ReLU) activation function, dropout, and batch normalization, this novel approach reduces the number of parameters of the CNN model, improves the non-linearity of the CNN model, and alleviates overfitting of the CNN model. In these ways, the accuracy of face recognition is increased. In the experiments, the proposed approach is compared with principal component analysis (PCA) and support vector machine (SVM) on ORL, Cohn-Kanade, and extended Yale-B face recognition data set, and it proves that this approach is promising.

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