A Novel Deep Learning Approach for Accurate Cancer Type and Subtype Identification-Reference-Cited by-同舟云学术

A Novel Deep Learning Approach for Accurate Cancer Type and Subtype Identification

Published:2024 Issue: Volume:12 Page:94116-94134
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Omar Bappi Jabed¹,Rony Mohammad Abu Tareq²^ORCID,Shariful Islam Mohammad³^ORCID,Alshathri Samah⁴^ORCID,El-Shafai Walid⁵^ORCID

Affiliation:

1. Department of Electrical and Electronics Engineering, Port City International University, Chittagong, Amtali

2. Department of Statistics, Noakhali Science and Technology University, Noakhali, Bangladesh

3. Department of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University, Noakhali, Bangladesh

4. Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, Saudi Arabia

5. Computer Science Department, Security Engineering Laboratory, Prince Sultan University, Riyadh, Saudi Arabia

Funder

Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia, through the Researchers Supporting Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10579976.pdf?arnumber=10579976

Reference33 articles.

1. A novel framework for lung cancer classification using lightweight convolutional neural networks and ridge extreme learning machine model with SHapley Additive exPlanations (SHAP)

2. Challenges and controversies in resectable non-small cell lung cancer: a clinician’s perspective

3. MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques

4. A review on computer aided detection and classification of leukemia

5. A Systematic Review on Recent Advancements in Deep and Machine Learning Based Detection and Classification of Acute Lymphoblastic Leukemia

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