Ada-GridRF: A Fast and Automated Adaptive Boost Based Grid Search Optimized Random Forest Ensemble model for Lung Cancer Detection.
Phys Eng Sci Med
; 45(3): 981-994, 2022 Sep.
Article
em En
| MEDLINE
| ID: mdl-35771385
Lung cancer is considered one of the leading causes of death all across the world. Various radiology-related fields increasingly have used Computer-aided diagnosis (CAD) systems. It just has already become a part of clinical work for lung cancer detection. In this article, we proposed an Adaptive Boost-based Grid Search Optimized Random Forest (Ada-GridRF) classifier that best optimized the hyperparameters of the base random forest model to identify the malignant and non-malignant nodules from the trained CT images. Improved performance speed and reduced computational complexity were the advantages of the proposed method. The proposed methodology was compared with other hyperparameter optimization techniques and also with different conventional approaches. It even outperformed the popular state-of-the-art deep learning techniques such as transfer learning and convolutional neural network. The experimental results proved that the proposed method yielded the best performance metrics of 97.97% accuracy, 100% sensitivity, 96% specificity, 96.08% precision, 98% F1-score, 4% False positives rate, and 99.8% Area under the ROC curve (AUC). It took only 8 msec to train the model. Thus, the proposed Ada-GridRF model can aid radiologists in fast lung cancer detection.
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Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Neoplasias Pulmonares
Tipo de estudo:
Clinical_trials
/
Diagnostic_studies
/
Prognostic_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article