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- W4312262673 abstract "Machine learning and deep learning have lately played a significant role in developing practical solutions in the field of medical care and treatment. Additionally, they enhance prediction precision in early and rapid illness diagnosis via the utilization of medical and audio-testing procedures. Given the shortage of qualified people in the medical field, physicians benefit from technology help, which allows them to visit more patients. Furthermore, it improves the efficiency with which pictures and one of the disease stages of lung cancer. Usually, cancer diseases are detected using a machine learning algorithm and the architecture of the Apache Spark design framework. T-BMSVM threshold technology supporting vector machine and SVM nonlinear binary classification using Radial Basis Function are used in conjunction with Multi-class WTA-SVM winner-take-all with threshold technology supporting vector machine T.BMSVM. RBF is used to investigate the categorization of nodules as malignant or benign and the presence of malignancies linked with the nodules. It is proposed that this article review analysis supports machine learning methods such as the Clinical Tree algorithm and Fuzzy C-Means Clustering (FCM). The and Cross-Validation with a small number of folds is used to improve various machine learning and deep learning approaches." @default.
- W4312262673 created "2023-01-04" @default.
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- W4312262673 date "2022-01-01" @default.
- W4312262673 modified "2023-09-29" @default.
- W4312262673 title "Comparative Analysis of Advanced Machine Learning Based Techniques to Identify the Lung Cancer: A Review" @default.
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- W4312262673 doi "https://doi.org/10.1007/978-3-031-21385-4_1" @default.
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