본문으로 건너뛰기
← 뒤로

A novel ensemble transfer learning approach for lung cancer classification using advance VGGNet16 with wavelet transform equalization & CL-PSO.

1/5 보강
Computers in biology and medicine 📖 저널 OA 7.2% 2021: 0/1 OA 2022: 0/5 OA 2023: 0/4 OA 2024: 3/8 OA 2025: 3/45 OA 2026: 1/32 OA 2021~2026 2026 Vol.200() p. 111338
Retraction 확인
출처

Das MN, Panda N, Rautray R, Tripathy J, Moreira F

ℹ️ 이 논문은 무료 전문이 아직 없습니다. 코퍼스 전체의 43.6%는 무료 가능 (통계 →) · 🏥 기관 EZproxy로 시도

📝 환자 설명용 한 줄

Lung cancer poses severe burden to the world and well-yield and requiring high-yield and easily deployable diagnostic strategies.

이 논문을 인용하기

↓ .bib ↓ .ris
APA Das MN, Panda N, et al. (2026). A novel ensemble transfer learning approach for lung cancer classification using advance VGGNet16 with wavelet transform equalization & CL-PSO.. Computers in biology and medicine, 200, 111338. https://doi.org/10.1016/j.compbiomed.2025.111338
MLA Das MN, et al.. "A novel ensemble transfer learning approach for lung cancer classification using advance VGGNet16 with wavelet transform equalization & CL-PSO.." Computers in biology and medicine, vol. 200, 2026, pp. 111338.
PMID 41370952 ↗

Abstract

Lung cancer poses severe burden to the world and well-yield and requiring high-yield and easily deployable diagnostic strategies. This study proposes an enhanced deep learning approach for early lung cancer diagnosis using a fine-tuned VGG-16 model optimized with Comprehensive Learning Particle Swarm Optimization (CL-PSO). To mitigate data imbalance and enhance feature visibility in CT scans, the framework introduces the Wavelet Transform Equalization in the preprocessing and utilizes class-weighted training to improve detection sensitivity, especially for the underrepresented benign cases. The model scored almost perfect classifications of the IQ-OTH/NCCD dataset with an accuracy of 99.99 %, precision and recall of 99.98 %, F1-score of 99.99 % and the AUC-ROC of 1.00. Grad-CAM visualizations further enhanced the model's interpretability and confirming that its predictions corresponded with radiological decision points. Apart from that, the model responded robustly to noise, occlusion, illumination, and below 50 ms per image. This results making model an ideal for real-time integration into imager based hospital PACS and edge based healthcare systems.

🏷️ 키워드 / MeSH 📖 같은 키워드 OA만

🏷️ 같은 키워드 · 무료전문 — 이 논문 MeSH/keyword 기반