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dc.contributor.authorMohamed Esmail, Karar-
dc.contributor.authorNawal, El-Fishawy-
dc.contributor.authorMarwa, Radad-
dc.date.accessioned2023-04-18T03:25:56Z-
dc.date.available2023-04-18T03:25:56Z-
dc.date.issued2023-
dc.identifier.urihttps://link.springer.com/article/10.1186/s13036-023-00340-0-
dc.identifier.urihttps://dlib.phenikaa-uni.edu.vn/handle/PNK/8029-
dc.descriptionCC BYvi
dc.description.abstractEarly diagnosis of Pancreatic Ductal Adenocarcinoma (PDAC) is the main key to surviving cancer patients. Urine proteomic biomarkers which are creatinine, LYVE1, REG1B, and TFF1 present a promising non-invasive and inexpensive diagnostic method of the PDAC. Recent utilization of both microfluidics technology and artificial intelligence techniques enables accurate detection and analysis of these biomarkers. This paper proposes a new deep-learning model to identify urine biomarkers for the automated diagnosis of pancreatic cancers. The proposed model is composed of one-dimensional convolutional neural networks (1D-CNNs) and long short-term memory (LSTM). It can categorize patients into healthy pancreas, benign hepatobiliary disease, and PDAC cases automatically.vi
dc.language.isoenvi
dc.publisherSpringervi
dc.subject1D-CNNsvi
dc.subjectLSTMvi
dc.titleAutomated classification of urine biomarkers to diagnose pancreatic cancer using 1-D convolutional neural networksvi
dc.typeBookvi
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OER - Kỹ thuật điện; Điện tử - Viễn thông

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