DBLCNN: Dependency-based lightweight convolutional neural network for multi-classification of breast histopathology images

2022 ◽  
Vol 73 ◽  
pp. 103451
Author(s):  
Chaoqing Wang ◽  
Weijun Gong ◽  
Junlong Cheng ◽  
Yurong Qian
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Histopathology Images ◽  
Multi Classification

scholarly journals Multi-Classification of Complex Microseismic Waveforms Using Convolutional Neural Network: A Case Study in Tunnel Engineering

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6762
Author(s):  
Hang Zhang ◽  
Jun Zeng ◽  
Chunchi Ma ◽  
Tianbin Li ◽  
Yelin Deng ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Microseismic Data ◽  
Proposed Model ◽  
Geological Conditions ◽  
Low Sensitivity ◽  
Short Time ◽  
Multi Classification ◽  
Microseismic Signal

Due to the complexity of the various waveforms of microseismic data, there are high requirements on the automatic multi-classification of such data; an accurate classification is conducive for further signal processing and stability analysis of surrounding rock masses. In this study, a microseismic multi-classification (MMC) model is proposed based on the short time Fourier transform (STFT) technology and convolutional neural network (CNN). The real and imaginary parts of the coefficients of microseismic data are inputted to the proposed model to generate three classes of targets. Compared with existing methods, the MMC has an optimal performance in multi-classification of microseismic data in terms of Precision, Recall, and F1-score, even when the waveform of a microseismic signal is similar to that of some special noise. Moreover, semisynthetic data constructed by clean microseismic data and noise are used to prove the low sensitivity of the MMC to noise. Microseismic data recorded under different geological conditions are also tested to prove the generality of the model, and a microseismic signal with Mw ≥ 0.2 can be detected with a high accuracy. The proposed method has great potential to be extended to the study of exploration seismology and earthquakes.

scholarly journals Insect Detection and Classification Based on an Improved Convolutional Neural Network

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4169 ◽  
Author(s):  
Denan Xia ◽  
Peng Chen ◽  
Bing Wang ◽  
Jun Zhang ◽  
Chengjun Xie
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Search Technique ◽  
Factors Affecting ◽  
Crop Fields ◽  
The Neural Network ◽  
Multi Classification ◽  
Expensive Process ◽  
Manual Classification

Regarding the growth of crops, one of the important factors affecting crop yield is insect disasters. Since most insect species are extremely similar, insect detection on field crops, such as rice, soybean and other crops, is more challenging than generic object detection. Presently, distinguishing insects in crop fields mainly relies on manual classification, but this is an extremely time-consuming and expensive process. This work proposes a convolutional neural network model to solve the problem of multi-classification of crop insects. The model can make full use of the advantages of the neural network to comprehensively extract multifaceted insect features. During the regional proposal stage, the Region Proposal Network is adopted rather than a traditional selective search technique to generate a smaller number of proposal windows, which is especially important for improving prediction accuracy and accelerating computations. Experimental results show that the proposed method achieves a heightened accuracy and is superior to the state-of-the-art traditional insect classification algorithms.

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