Automated visual inspection for surface appearance defects of varistors using an adaptive neuro-fuzzy inference system

Su, Jianbo; Tarng, Y.S.

doi:10.1007/s00170-006-0756-3

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Image Processing Algorithm Research and Testing1

Cervical Cancer Diagnosis Based On Improved Segmentation And...1

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2022

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Cited by 5 publications

(3 citation statements)

References 15 publications

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“…Many recent researches have made remarkable contributions on both surface defect detection [26,27] and classification [28]. For the purpose to maximize the advantages of FPGA and IPC, this section concentrates on the overall framework of image processing (shown in Fig.…”

Section: Image Processing Algorithm Research and Testingmentioning

confidence: 99%

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A cost-effective and automatic surface defect inspection system for hot-rolled flat steel

Luo

2016

Robotics and Computer-Integrated Manufacturing

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Section: Image Processing Algorithm Research and Testingmentioning

confidence: 99%

“…Luo, Y. He / Robotics and Computer-Integrated Manufacturing 38 (2016)[16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] …”

mentioning

confidence: 99%

A cost-effective and automatic surface defect inspection system for hot-rolled flat steel

Luo

2016

Robotics and Computer-Integrated Manufacturing

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“…The proposed cervical cancer diagnosis model utilizes the ANFIS 38 classifier for classifying the extracted features

{FE}_h^{ext}

into multiple classes. ANFIS is known to be a fuzzy Sugeno model that facilitates adaptation and learning.…”

Section: Cervical Cancer Diagnosis Based On Improved Segmentation And...mentioning

confidence: 99%

Adaptive fuzzy‐region growing fusion and improved CNN‐ANFIS‐based automated segmentation and classification of cervical cancer

Jeyshri

Kowsigan

2022

Concurrency and Computation

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Summary The cervical cancer screening mechanisms help to classify cervical lesions into cancerous, precancerous, and normal. But, some of the classification approaches provide optimal outcomes based on the precise spotting and segmenting of cervical cancer. Moreover, still, challenges persist in the precise spotting of cervical cancer. The conventional screening methods for cervical cancer require more experienced pathologists, but it may also lead to poor efficiency and low accuracy. To overcome these issues, medical image processing introduces both machine learning and deep learning methods into cervical cell classification. The major scope of this task is to plan a new cervical cancer diagnosis method using intelligent approaches. Here, the segmentation of abnormality is done by the novel adaptive fuzzy‐region growing fusion model, which is improved by the hybridized optimization algorithm named gray wolf‐tunicate swarm algorithm (GW‐TSA). Then, the new ensemble classification termed Improved convolutional neural network with artificial neural network fuzzy inference system, which depends on the proposed GW‐TSA for performance enhancement through optimizing the hyperparameters. Finally, the simulation findings show that the designed approach is achievable and provides the highest accuracy rate in diagnosing cervical cancer.

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Automated inspection of engineering ceramic grinding surface damage based on image recognition

Chen

Lin

Han

et al. 2012

Int J Adv Manuf Technol

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Automated visual inspection for surface appearance defects of varistors using an adaptive neuro-fuzzy inference system

Cited by 5 publications

References 15 publications

A cost-effective and automatic surface defect inspection system for hot-rolled flat steel

A cost-effective and automatic surface defect inspection system for hot-rolled flat steel

Adaptive fuzzy‐region growing fusion and improved CNN‐ANFIS‐based automated segmentation and classification of cervical cancer

Automated inspection of engineering ceramic grinding surface damage based on image recognition

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