Dataset Information

A system based on deep convolutional neural network improves the detection of early gastric cancer.

ABSTRACT:

Background

Early gastric cancer (EGC) has a high survival rate, but it is difficult to diagnosis. Recently, artificial intelligence (AI) based on deep convolutional neural network (DCNN) has made significant progress in the field of gastroenterology. The purpose of this study was to establish a DCNN assist system to improve the detection of EGC.

Methods

3400 EGC and 8600 benign images were collected to train the DCNN to detect EGC. Subsequently, its diagnostic ability was compared to that of endoscopists using an independent internal test set (ITS, including 1289 images) and an external test set (ETS, including 542 images) come from three digestive center.

Results

The diagnostic time of DCNN and endoscopists were 0.028s, 8.05 ± 0.21s, 7.69 ± 0.25s in ITS, and 0.028s, 7.98 ± 0.19s, 7.50 ± 0.23s in ETS, respectively. In ITS, the diagnostic sensitivity and accuracy of DCNN are 88.08%(95% confidence interval,95%CI,85.24%-90.44%), 88.60% (95%CI,86.74%-90.22%), respectively. In ETS, the diagnostic sensitivity and accuracy are 92.08% (95%CI, 87.91%- 94.94%),92.07%(95%CI, 89.46%-94.08%),respectively. DCNN outperformed all endoscopists in ETS, and had a significantly higher sensitivity than the junior endoscopists(JE)(by18.54% (95%CI, 15.64%-21.84%) in ITS, also higher than JE (by21.67%,95%CI, 16.90%-27.32%) and senior endoscopists (SE) (by2.08%, 95%CI, 0.75%-4.92%)in ETS. The accuracy of DCNN model was higher (by10.47%,95%CI, 8.91%-12.27%) than that of JE in ITS, and also higher (by14.58%,95%CI, 11.84%-17.81%; by 1.94%,95%CI,1.25%-2.96%, respectively) than JE and SE in ETS.

Conclusion

The DCNN can detected more EGC images in a shorter time than the endoscopists. It will become an effective tool to assist in the detection of EGC in the near future.

SUBMITTER: Feng J

PROVIDER: S-EPMC9815521 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

A system based on deep convolutional neural network improves the detection of early gastric cancer.

Feng Jie J Yu Shang Rui SR Zhang Yao Ping YP Qu Lina L Wei Lina L Wang Peng Fei PF Zhu Li Juan LJ Bao Yanfeng Y Lei Xiao Gang XG Gao Liang Liang LL Feng Yan Hu YH Yu Yi Y Huang Xiao Jun XJ

Frontiers in oncology 20221222

<h4>Background</h4>Early gastric cancer (EGC) has a high survival rate, but it is difficult to diagnosis. Recently, artificial intelligence (AI) based on deep convolutional neural network (DCNN) has made significant progress in the field of gastroenterology. The purpose of this study was to establish a DCNN assist system to improve the detection of EGC.<h4>Methods</h4>3400 EGC and 8600 benign images were collected to train the DCNN to detect EGC. Subsequently, its diagnostic ability was compared ...[more]

PMID: 36620563

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Project description:BackgroundsConventional ultrasound manual scanning and artificial diagnosis approaches in breast are considered to be operator-dependence, slight slow and error-prone. In this study, we used Automated Breast Ultrasound (ABUS) machine for the scanning, and deep convolutional neural network (CNN) technology, a kind of Deep Learning (DL) algorithm, for the detection and classification of breast nodules, aiming to achieve the automatic and accurate diagnosis of breast nodules.MethodsTwo hundred and ninety-three lesions from 194 patients with definite pathological diagnosis results (117 benign and 176 malignancy) were recruited as case group. Another 70 patients without breast diseases were enrolled as control group. All the breast scans were carried out by an ABUS machine and then randomly divided into training set, verification set and test set, with a proportion of 7:1:2. In the training set, we constructed a detection model by a three-dimensionally U-shaped convolutional neural network (3D U-Net) architecture for the purpose of segment the nodules from background breast images. Processes such as residual block, attention connections, and hard mining were used to optimize the model while strategies of random cropping, flipping and rotation for data augmentation. In the test phase, the current model was compared with those in previously reported studies. In the verification set, the detection effectiveness of detection model was evaluated. In the classification phase, multiple convolutional layers and fully-connected layers were applied to set up a classification model, aiming to identify whether the nodule was malignancy.ResultsOur detection model yielded a sensitivity of 91% and 1.92 false positive subjects per automatically scanned imaging. The classification model achieved a sensitivity of 87.0%, a specificity of 88.0% and an accuracy of 87.5%.ConclusionsDeep CNN combined with ABUS maybe a promising tool for easy detection and accurate diagnosis of breast nodule.

Dataset Information

A system based on deep convolutional neural network improves the detection of early gastric cancer.

Background

Methods

Results

Conclusion

Publications

A system based on deep convolutional neural network improves the detection of early gastric cancer.

Similar Datasets

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