Gene-based lung cancer detection system through omix data and optimized convolutional neural network.
1/5 보강
PICO 자동 추출 (휴리스틱, conf 2/4)
유사 논문P · Population 대상 환자/모집단
The developed technique attains high accuracy and high precision of 99.2% and 99%.
I · Intervention 중재 / 시술
추출되지 않음
C · Comparison 대조 / 비교
추출되지 않음
O · Outcome 결과 / 결론
The developed method is implemented in the Matlab tool, and the improved performance is compared to other existing methods. The developed technique attains high accuracy and high precision of 99.2% and 99%.
Among the cancers that pose the greatest threat to life worldwide is lung cancer.
APA
Vasanthi M, Aldahwan NS (2026). Gene-based lung cancer detection system through omix data and optimized convolutional neural network.. Journal of computer-aided molecular design, 40(1). https://doi.org/10.1007/s10822-026-00768-8
MLA
Vasanthi M, et al.. "Gene-based lung cancer detection system through omix data and optimized convolutional neural network.." Journal of computer-aided molecular design, vol. 40, no. 1, 2026.
PMID
41772156 ↗
Abstract 한글 요약
Among the cancers that pose the greatest threat to life worldwide is lung cancer. According to estimates from the World Cancer Research Fund International, there will be 1.8 million new instances of this disease diagnosed in 2022. When medical personnel diagnose and classify patients' conditions proactively, they may treat them safely and efficiently. The advent of the microarray method has made it possible to examine the connections between genes and various diseases, including lung malignancies. Numerous methods have been developed to forecast gene-based diseases, but they still have problems with high computational cost, time consumption, complex data, and inaccurate prediction. Therefore, create an efficient lung cancer detection system in this research by designing an Improved Convolutional Neural Network with Honey Bee Mating Optimization (ICNN-HBMO). First, the system is trained using Omix data, and the dataset is normalized using min-max normalization. Then Kernel Principal Component Analysis (KPCA) technique is employed for feature reduction. Furthermore, an enhanced CNN is employed to classify lung cancer using HBMO. The HBMO algorithm optimizes the weight and bias parameters of the ICNN to improve prediction performance. The developed method is implemented in the Matlab tool, and the improved performance is compared to other existing methods. The developed technique attains high accuracy and high precision of 99.2% and 99%.
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🏷️ 같은 키워드 · 무료전문 — 이 논문 MeSH/keyword 기반
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