Item Infomation
Title: | Automated classification of urine biomarkers to diagnose pancreatic cancer using 1-D convolutional neural networks |
Authors: | Mohamed Esmail, Karar Nawal, El-Fishawy Marwa, Radad |
Issue Date: | 2023 |
Publisher: | Springer |
Abstract: | Early 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. |
Description: | CC BY |
URI: | https://link.springer.com/article/10.1186/s13036-023-00340-0 https://dlib.phenikaa-uni.edu.vn/handle/PNK/8029 |
Appears in Collections | OER - Kỹ thuật điện; Điện tử - Viễn thông |
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