Gastrointestinal Cancer Detection and Classification Using African Vulture Optimization Algorithm With Transfer Learning-Reference-Cited by-同舟云学术

Gastrointestinal Cancer Detection and Classification Using African Vulture Optimization Algorithm With Transfer Learning

Published:2024 Issue: Volume:12 Page:23122-23131
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Almasoud Ahmed S.¹^ORCID,Maray Mohammed²^ORCID,Alkahtani Hend Khalid³,Alotaibi Faiz Abdullah⁴^ORCID,Alnfiai Mrim M.⁵^ORCID,Sayed Ahmed⁶

Affiliation:

1. Department of Information Systems, College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia

2. Department of Information Systems, College of Computer Science, King Khalid University, Abha, Saudi Arabia

3. Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box, 84428, Riyadh, Saudi Arabia

4. Department of Information Science, College of Humanities and Social Sciences, King Saud University, Riyadh, Saudi Arabia

5. Department of Information Technology, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia

6. Research Center, Future University in Egypt, New Cairo, Egypt

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10385084.pdf?arnumber=10385084

Reference34 articles.

1. An application of raman spectroscopy in combination with machine learning to determine gastric cancer spectroscopy marker

2. Deep learning‐based AI model for signet‐ring cell carcinoma diagnosis and chemotherapy response prediction in gastric cancer

3. Identifying early gastric cancer under magnifying narrow-band images with deep learning: a multicenter study

4. Computed Tomography-Based Deep Learning Nomogram Can Accurately Predict Lymph Node Metastasis in Gastric Cancer

5. Identification of gastric cancer with convolutional neural networks: a systematic review