Diagnosis System of Microscopic Hyperspectral Image of Hepatobiliary Tumors Based on Convolutional Neural Network

Huang, Shixin; Luo, Jiawei; Pu, Kexue; Wu, Min

doi:10.1155/2022/3794844

Cited by 3 publications

(3 citation statements)

References 25 publications

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“…Convolutional neural network [22][23][24][25] is a multilayer feedforward neural network composed of input, hidden, and output layers. Suppose the convolutional network's input and output dimensions are m and 1, respectively, and the number of hidden layers is p. en, the mapping mathematical expression of the convolutional neural network is shown in…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

DA-CNN-Driven Innovative Ideological Politics Education Management System

Yingkun

Liang

Chu³

2022

Mobile Information Systems

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With the rapid development of the Internet, the network has become an important part of the study, work, and life of all teachers and students in colleges and universities. It also brings great challenges to the traditional way of ideological and political education in colleges and universities. Colleges and universities have launched various beneficial attempts in the ideological and political education into the network. In this context, in order to improve the evaluation accuracy of ideological and political classroom teaching quality in colleges and universities, this paper puts forward an evaluation method of ideological and political classroom teaching quality in colleges and universities based on DA-CNN. On the basis of constructing the evaluation index system of ideological and political classroom teaching quality in colleges and universities by analytic hierarchy process, the scores of 25 evaluation indexes affecting the ideological and political teaching quality in colleges and universities are taken as the input of DA-CNN, and the comprehensive score of ideological and political teaching quality in colleges and universities is taken as the output of DA-CNN. At the same time, an innovative ideological and political education management system based on convolutional neural network is designed and developed, and the functional modules and database of the system are planned.

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Section: Convolutional Neural Networkmentioning

confidence: 99%

DA-CNN-Driven Innovative Ideological Politics Education Management System

Yingkun

Liang

Chu³

2022

Mobile Information Systems

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“…Furthermore, we have introduced additional clustering metrics to comprehensively evaluate the clustering results of different algorithms. These metrics include Accuracy (ACC) [42], Adjusted Mutual Information (AMI) [43], and Adjusted Rand Index (ARI) [44]. The accuracy rate, ACC, measures the ratio of correctly clustered records to the total number of records, and its calculation formula is provided in (12).…”

Section: Clustering Evaluation Indicatorsmentioning

confidence: 99%

WOA-DBSCAN: Application of Whale Optimization Algorithm in DBSCAN Parameter Adaption

Zhang,

Zhou

2023

IEEE Access

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Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a classic density-based clustering method that can identify clusters of arbitrary shapes in noisy datasets. However, DBSCAN requires two input parameters: the neighborhood distance value (Eps) and the minimum number of sample points in its neighborhood (MinPts), to perform clustering on a dataset. The quality of clustering is highly sensitive to these two parameters. To tackle this issue, this paper introduces a parameter-adaptive DBSCAN clustering algorithm based on the Whale Optimization Algorithm (WOA-DBSCAN). The algorithm determines the parameter range based on the dataset distribution and utilizes the silhouette coefficient as the objective function. It iteratively selects the two input parameters of DBSCAN within the parameter range using the WOA. This approach ultimately achieves adaptive clustering of DBSCAN. Experimental results on five typical artificial datasets and six real UCI datasets demonstrate the effectiveness of the proposed WOA-DBSCAN algorithm. Compared with DBSCAN and its related optimization algorithms, WOA-DBSCAN shows significant improvements. The F-values of WOA-DBSCAN increased by 9.8%, 13.2%, and 2% respectively in two-dimensional artificial datasets. Additionally, the accuracy values on low to medium dimensional real datasets increased by 22.3%, 10%, and 23.3%. Hence, WOA-DBSCAN can maintain the clustering ability of DBSCAN while achieving adaptive parameter clustering.

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“…e main principal of convolutional neural network is the face feature extraction and the training of neural network model, so the structure of convolutional neural network will determine the effect of face recognition behind [15][16][17][18][19][20][21][22][23][24][25][26][27][28]. e convolutional neural network system has designed eight layers of neural network, including three convolutional layers, three pooling layers, one fully connected layer and one output layer.…”

Section: 31mentioning

confidence: 99%

Face Recognition Model Design Based on Convolutional Neural Networks

Zhang

2022

Scientific Programming

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With the rapid development of computer science and Internet technology, face recognition technology is widely used in such as public security, judicial and criminal investigation, public security, information security and access control system, such as public security system need to find out the criminals in the system library, or from the entrance control system quickly identify and match the identity of relevant personnel information. As a stable, intuitive and highly recognizable biometric feature, human face is being paid more and more attention by researchers. Compared with other bioinformation recognition methods, face recognition is characterized by direct, friendliness and convenience. Users are not easy to resist, and compared with other recognition technologies, they are easy to be accepted by users, so it has received attention and research. This paper designs a face recognition system based on a convolutional neural network. Compared with the traditional face recognition method, the convolutional neural network model does not need manual complex and time-consuming feature extraction algorithm design, only need to design an effective neural network model, and then end-to-end training on a large number of training samples for simple and efficient training, can get a good identification effect. The design uses the target detection algorithm to conduct accurate, real-time, efficient face recognition, and can accurately identify the people in the camera.

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Diagnosis System of Microscopic Hyperspectral Image of Hepatobiliary Tumors Based on Convolutional Neural Network

Cited by 3 publications

References 25 publications

DA-CNN-Driven Innovative Ideological Politics Education Management System

DA-CNN-Driven Innovative Ideological Politics Education Management System

WOA-DBSCAN: Application of Whale Optimization Algorithm in DBSCAN Parameter Adaption

Face Recognition Model Design Based on Convolutional Neural Networks

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