Name
Abstract | ||
|---|---|---|
Identifying and diagnosing as early as possible malignant lung nodules is essential to reduce the mortality of lung cancer patients. Radiologists employ computer tomography scan to detect cancer in the body and track its growth. Interpretation of tomography scan, today still not automated, can lead to cancer detection at early stages, thus leading to the treatment of cancer which can decrease the death rates. Image processing, a branch of computer-assisted diagnostic, can support radiologists for the early detection of cancer. Against that background, we propose a novel ensemble-based approach for more accurate lung cancer classification using Computer tomography scan images. This work exploits transfer learning using pre-trained deep networks (e.g., VGG, Xception, and ResNet), combined into an ensemble architecture to classify clustered images of lung lobes. The approach is validated on a real dataset and shows that the ensemble classifier ensures effective performance, exhibiting better generalization capabilities. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/IJCNN52387.2021.9534176 | 2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) |
Keywords | DocType | ISSN |
Deep Learning, CT scan Images, Lung Cancer | Conference | 2161-4393 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pasquale Ardimento | 1 | 48 | 10.47 |
| Lerina Aversano | 2 | 670 | 53.19 |
| Mario Luca Bernardi | 3 | 156 | 29.89 |
| Marta Cimitile | 4 | 183 | 24.34 |