Lung-EffNet: Lung cancer classification using EfficientNet from CT-scan images
Nanjing University of Science and Technology · University of Management and Technology · +3 more institutions
Abstract
Lung cancer (LC) remains a leading cause of death worldwide. Early diagnosis is critical to protect innocent human lives. Computed tomography (CT) scans are one of the primary imaging modalities for lung cancer diagnosis. However, manual CT scan analysis is time-consuming and prone to errors/not accurate. Considering these shortcomings, computational methods especially machine learning and deep learning algorithms are leveraged as an alternative to accelerate the accurate detection of CT scans as cancerous, and non-cancerous. In the present article, we proposed a novel transfer learning-based predictor called, Lung-EffNet for lung cancer classification. Lung-EffNet is built based on the architecture of…
Citation impact
- FWCI
- 56.95
- Percentile
- 100%
- References
- 39
Authors
7- RRRehan Raza
Nanjing University of Science and Technology, University of Management and Technology
- FZFatima Zulfiqar
COMSATS University Islamabad, Bahria University
- MOMuhammad Owais Khan
COMSATS University Islamabad, University of Management and Technology
- MAMuhammad Arif
Hamad bin Khalifa University
- AAAtif Alvi
University of Management and Technology
Topics & keywords
- Computer science
- Lung cancer screening
- Lung cancer
- Transfer of learning
- Benchmark (surveying)
- Artificial intelligence
- Lung
- Computed tomography
- Good health and well-being