A Review on Lung Cancer Classification Using Deep Learning Techniques
Cancer of the lung system is arguably the second largest factor in the worldwide fatality explosion. It originates from smoking too much and tobacco use. Uncontrolled cell growth in the lung region will hurt human survival. The exponential increase in medical reports might challenge the manual inter...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10977947/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Cancer of the lung system is arguably the second largest factor in the worldwide fatality explosion. It originates from smoking too much and tobacco use. Uncontrolled cell growth in the lung region will hurt human survival. The exponential increase in medical reports might challenge the manual interpretation of disease prediction. Therefore, using Computer-Aided Diagnosis (CAD) approaches, a tumor might be detected early from an appropriate appearance. This study thoroughly evaluates various lung cancer detection methods for classifying a nodule. The area of lung cancer prediction is becoming increasingly popular due to the many benefits of automated medical diagnosis using Deep Learning techniques. This study discusses potential deep learning (DL) strategies for lung disease applications, an overview of DL approaches, and novel elements of the methods under investigation. Classification and segmentation are the two main deep learning techniques for lung cancer detection and screening, which are the focus of this review. We will also talk about the benefits and drawbacks of contemporary deep learning models. The resulting study demonstrates the significant potential of deep learning approaches for exact and efficient computerized lung cancer monitoring and detection employing CT scans. Following this study, an overview of prospective further studies is given, focused on enhancing deep learning to advance automated techniques for diagnosing lung cancer. |
|---|---|
| ISSN: | 2169-3536 |