Deep Learning Applications in Computed Tomography Images for Pulmonary Nodule Detection and Diagnosis: A Review
Problem
Lung cancer has one of the highest mortality rates of all cancers, and early detection of pulmonary nodules in CT scans is crucial for survival. The paper reviews computer-aided diagnosis systems that detect and classify such nodules.
Why it matters
Early nodule diagnosis is crucial to improving patient survival rates for one of the deadliest cancers.
What the AI did
The paper surveys deep learning CAD systems for lung nodule segmentation and classification (nodule/non-nodule, benign/malignant), organizing the literature by nodule type and network architecture.
Results
not stated in source
Impact
Organizes prior CAD literature on nodule segmentation and classification; no new results claimed.
Record
| Venue | Diagnostics |
|---|---|
| Published | 2022 |
| Work type | article |
| Authors | Rui Li, Chuda Xiao, Yongzhi Huang, Haseeb Hassan, Bingding Huang |
| Citations | 129 (OpenAlex, at import time) |
| References | 112 |
| Model or method | CNN architectures (reviewed, e.g., multiview CNN) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/diagnostics12020298 |
| Open access | gold |
| Keywords | deep learning, pulmonary nodules, computed tomography, CAD, lung cancer, review |
Importance rating
major — millions of lives, an industry or a field
Abstract states lung cancer has one of the highest mortality rates of all cancers; early nodule detection is crucial to survival, affecting a major patient population.