Search

Current filters:




Current filters:




Search Results

Results 1-1 of 1 (Search time: 0.0 seconds).
  • <<
  • 1
  • >>
    • Automated classification of urine biomarkers to diagnose pancreatic cancer using 1-D convolutional neural networks
    Authors: Mohamed Esmail, Karar; Nawal, El-Fishawy; Marwa, Radad;  Advisor: -;  Co-Author: - (2023)

    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.