PortNet: Achieving lightweight architecture and high accuracy in lung cancer cell classification
Background: As one of the cancers with the highest incidence and mortality rates worldwide, the timeliness and accuracy of cell type diagnosis in lung cancer are crucial for patients' treatment decisions. This study aims to develop a novel deep learning model to provide efficient, accurate, and...
Saved in:
Main Authors: | Kaikai Zhao, Youjiao Si, Liangchao Sun, Xiangjiao Meng |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2025-02-01
|
Series: | Heliyon |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844025002300 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Molecular and pathological identification of ovine pulmonary adenomatosis at Mosul city abattoirs
by: Osama Z. Jiad, et al.
Published: (2025-01-01) -
Adult-type rhabdomyoma of the lung: A case report
by: Yimin Wu, et al.
Published: (2024-04-01) -
Protocol to boost the robustness and accuracy of spatial transcriptomics algorithms using ensemble techniques
by: Jiazhang Cai, et al.
Published: (2025-03-01) -
Lung cancer classification with Convolutional Neural Network Architectures
by: Shivan H. M. Mohammed, et al.
Published: (2021-02-01) -
Gene mutation, clinical characteristics and pathology in resectable lung adenocarcinoma
by: Ji’an Zou, et al.
Published: (2025-01-01)