Gas-Net: A deep neural network for gastric tumor semantic segmentation

Lamia Fatiha KAZI TANI; Mohammed Yassine KAZI TANI; Benamar KADRI; Lamia Fatiha KAZI TANI; Mohammed Yassine KAZI TANI; Benamar KADRI

doi:10.3934/bioeng.2022018

AIMS Bioengineering

2022, Volume 9, Issue 3: 266-282. doi: 10.3934/bioeng.2022018

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Research article

Gas-Net: A deep neural network for gastric tumor semantic segmentation

1.
Biomedical Engineering dept. STIC Laboratory, University of Tlemcen, Tlemcen, Algeria
2.
LabRI-SBA Laboratory, The Higher School in Computer Science 08 May 1945 of Sidi Bel, Algeria

Received: 20 May 2022 Revised: 19 August 2022 Accepted: 22 August 2022 Published: 08 September 2022

Currently, the gastric cancer is the source of the high mortality rate where it is diagnoses from the stomach and esophagus tests. To this end, the whole of studies in the analysis of cancer are built on AI (artificial intelligence) to develop the analysis accuracy and decrease the danger of death. Mostly, deep learning methods in images processing has made remarkable advancement. In this paper, we present a method for detection, recognition and segmentation of gastric cancer in endoscopic images. To this end, we propose a deep learning method named GAS-Net to detect and recognize gastric cancer from endoscopic images. Our method comprises at the beginning a preprocessing step for images to make all images in the same standard. After that, the GAS-Net method is based an entire architecture to form the network. A union between two loss functions is applied in order to adjust the pixel distribution of normal/abnormal areas. GAS-Net achieved excellent results in recognizing lesions on two datasets annotated by a team of expert from several disciplines (Dataset1, is a dataset of stomach cancer images of anonymous patients that was approved from a private medical-hospital clinic, Dataset2, is a publicly available and open dataset named HyperKvasir ‎[1]). The final results were hopeful and proved the efficiency of the proposal. Moreover, the accuracy of classification in the test phase was 94.06%. This proposal offers a specific mode to detect, recognize and classify gastric tumors.
- endoscopic images,
- artificial intelligence,
- deep learning,
- gastric tumor, segmentation
Citation: Lamia Fatiha KAZI TANI, Mohammed Yassine KAZI TANI, Benamar KADRI. Gas-Net: A deep neural network for gastric tumor semantic segmentation[J]. AIMS Bioengineering, 2022, 9(3): 266-282. doi: 10.3934/bioeng.2022018

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Abstract

Currently, the gastric cancer is the source of the high mortality rate where it is diagnoses from the stomach and esophagus tests. To this end, the whole of studies in the analysis of cancer are built on AI (artificial intelligence) to develop the analysis accuracy and decrease the danger of death. Mostly, deep learning methods in images processing has made remarkable advancement. In this paper, we present a method for detection, recognition and segmentation of gastric cancer in endoscopic images. To this end, we propose a deep learning method named GAS-Net to detect and recognize gastric cancer from endoscopic images. Our method comprises at the beginning a preprocessing step for images to make all images in the same standard. After that, the GAS-Net method is based an entire architecture to form the network. A union between two loss functions is applied in order to adjust the pixel distribution of normal/abnormal areas. GAS-Net achieved excellent results in recognizing lesions on two datasets annotated by a team of expert from several disciplines (Dataset1, is a dataset of stomach cancer images of anonymous patients that was approved from a private medical-hospital clinic, Dataset2, is a publicly available and open dataset named HyperKvasir ‎[1]). The final results were hopeful and proved the efficiency of the proposal. Moreover, the accuracy of classification in the test phase was 94.06%. This proposal offers a specific mode to detect, recognize and classify gastric tumors.

Acknowledgments

This research was partially supported by the Ministry of higher education and scientific research who provided insight and expertise that greatly assisted the research.

Conflict of interest

Authors declare that they have no conflict of interest.

Author contributions

Lamia Fatiha KAZI TANI, Mohammed Yassine KAZI TANI and Benamar KADRI contribute to realize the presented idea. Lamia Fatiha KAZI TANI developed the theory, achieved the programs and verified the critical methods. Mohammed Yassine KAZI TANI and Benamar KADRI supervised the results of this work. All authors discussed the results and contributed to the final manuscript.

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