Automatic Polyp Detection in Colonoscopy Images: Convolutional Neural Network, Dataset and Transfer Learning

Sun, Mingming; Zhang, Xiao; Qu, Ge; Zou, Mengshu; Du, Hai Lian; Ma, Liyong; Qu, Yao

doi:10.1166/jmihi.2019.2550

Cited by 3 publications

(3 citation statements)

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“…With the rapid development of deep learning technology [ 12 , 13 , 14 , 15 ], it has made great progress in image segmentation, classification, and recognition [ 16 , 17 , 18 , 19 ]. Gastric cancer and polyps have been widely studied as their high potential risk [ 20 , 21 , 22 , 23 , 24 , 25 , 26 ]. Some researchers have studied the image quality of endoscopy to ensure the reliability of gastroscopic examination [ 27 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based anatomical position recognition for gastroscopic examination

Su,

Liu,

Jiang

et al. 2024

THC

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BACKGROUND: The gastroscopic examination is a preferred method for the detection of upper gastrointestinal lesions. However, gastroscopic examination has high requirements for doctors, especially for the strict position and quantity of the archived images. These requirements are challenging for the education and training of junior doctors. OBJECTIVE: The purpose of this study is to use deep learning to develop automatic position recognition technology for gastroscopic examination. METHODS: A total of 17182 gastroscopic images in eight anatomical position categories are collected. Convolutional neural network model MogaNet is used to identify all the anatomical positions of the stomach for gastroscopic examination The performance of four models is evaluated by sensitivity, precision, and F1 score. RESULTS: The average sensitivity of the method proposed is 0.963, which is 0.074, 0.066 and 0.065 higher than ResNet, GoogleNet and SqueezeNet, respectively. The average precision of the method proposed is 0.964, which is 0.072, 0.067 and 0.068 higher than ResNet, GoogleNet, and SqueezeNet, respectively. And the average F1-Score of the method proposed is 0.964, which is 0.074, 0.067 and 0.067 higher than ResNet, GoogleNet, and SqueezeNet, respectively. The results of the t-test show that the method proposed is significantly different from other methods (p< 0.05). CONCLUSION: The method proposed exhibits the best performance for anatomical positions recognition. And the method proposed can help junior doctors meet the requirements of completeness of gastroscopic examination and the number and position of archived images quickly.

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Section: Introductionmentioning

confidence: 99%

Deep learning-based anatomical position recognition for gastroscopic examination

Su,

Liu,

Jiang

et al. 2024

THC

Self Cite

View full text Add to dashboard Cite

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“…With the development of machine learning recently, deep learning technology has shown impressive results in image classification applications [29][30][31][32][33][34][35][36]. As a machine learning technology, deep learning simulates the human brain.…”

Section: Introductionmentioning

confidence: 99%

“…In these machine learning methods, the selected features determine the accuracy of the classification [27,28]. These features are selected by hand, and how to select these features is a difficult problem.With the development of machine learning recently, deep learning technology has shown impressive results in image classification applications [29][30][31][32][33][34][35][36]. As a machine learning technology, deep learning simulates the human brain.…”

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confidence: 99%

Blister Defect Detection Based on Convolutional Neural Network for Polymer Lithium-Ion Battery

Xie

Zhang

2019

Applied Sciences

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To ensure the quality and reliability of polymer lithium-ion battery (PLB), automatic blister defect detection instead of manual detection is developed in the production of PLB cell sheets. A convolutional neural network (CNN) based detection method is proposed to detect blister in cell sheets employing cell sheet images. An improved architecture for dense block and a learning method based on optimization of learning rate are discussed. The proposed method was superior to other machine learning based methods when the classification performance and confusion matrix were compared in experiments. The proposed CNN method had the best defect detection performance and real-time performance for industry field application. Keywords: defect detection; polymer lithium-ion battery; convolutional neural network; deep learning; blister defect; flower pollination algorithm IntroductionThe application of lithium-ion batteries has changed consumer electronic products, which greatly reduce the weight and volume of mobile phones, notebooks, and other portable products. Lithium-ion batteries have been widely studied, including model, optimal design, and so on [1][2][3][4][5][6]. At present, the most commonly used lithium-ion battery is polymer lithium-ion battery (PLB). The cathode materials of PLB are commonly lithium cobalt acid, lithium manganese acid, ternary materials or lithium iron phosphate. The anode materials usually use carbon materials, such as artificial graphite, natural graphite, intermediate phase carbon microspheres, petcoke, carbon fiber, pyrolysis resin carbon and so on. C-rate is the measurement of the charge and discharge current with respect to its nominal capacity. At present, most PLB use polymer gel electrolyte instead of liquid electrolyte, which makes PLB have the advantages of thinning, arbitrary area and arbitrary shape. These characteristics improve the capacity of PLB, and PLB has the characteristics of miniaturization, thinning, and light quantification. PLB has been widely used in portable electronic equipment, and it is gradually being applied to more fields. With the application of PLB in more and more electronic products, the quality of PLB has more and more influence on the quality of electronic products. The quality of PLB is critical to the quality and reliability of electronic products.Recently, an automatic line of PLB has been developed in Dongsheng Energy Corporation, Weihai, China. The PLB has a voltage of 48 V, a charging current of 2-3 A, and nominal capacity of 16 AH. The anode of PLB is a conductive high molecular polymer, the catrode is graphite, and a colloidal polymer electrolyte is used. During the production process of the PLB, several cathode pieces, anode pieces, and separator pieces are combined to produce a cell sheet. Great battery capacity can be provided with more cell sheets combined together. Thus, the quality inspection of cell sheets in automated production lines is essential to the quality of the final battery product. The cell sheet defect needs to be detected to ens...

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Minimum redundancy maximum relevance (mRMR) based feature selection from endoscopic images for automatic gastrointestinal polyp detection

Billah

Waheed

2020

Multimed Tools Appl

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Automatic Polyp Detection in Colonoscopy Images: Convolutional Neural Network, Dataset and Transfer Learning

Cited by 3 publications

References 0 publications

Deep learning-based anatomical position recognition for gastroscopic examination

Deep learning-based anatomical position recognition for gastroscopic examination

Blister Defect Detection Based on Convolutional Neural Network for Polymer Lithium-Ion Battery

Minimum redundancy maximum relevance (mRMR) based feature selection from endoscopic images for automatic gastrointestinal polyp detection

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