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 |
ABSTRACTS VIEWS
41
FULLTEXT VIEWS
40
Files in This Item: