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Efficient Machine Learning Techniques to Predict Lung Cancer

Published: 11 August 2022 Publication History

Abstract

One of the most difficult to diagnose and one of the deadliest diseases is lung cancer. A big reason for this is that it takes a long time to identify at an early stage. For treatment, a rapid and precise diagnosis of nodules is very crucial. In order to identify cancer in its early stages, a variety of techniques have been employed. Machine learning approaches were used in this work in order to identify lung cancer nodules. We used machine learning algorithms such as LightGBM, XGBoost, K-Nearest Neighbors, Support Vector Machines, Naïve Bayes, and Random Forest to discover anomalous data. We compared all of the approaches. The results of the experiments reveal that LightGBM produces the greatest outcomes with 99.91 percent accuracy, 0.001261 loss and XGBoost outcomes with 99.86 percent accuracy, 0.001446 loss.

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  • (2023)Deep Learning-Assisted Lung Cancer Diagnosis from Histopathology Images2023 IEEE 5th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS)10.1109/ECBIOS57802.2023.10218594(17-20)Online publication date: 2-Jun-2023

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cover image ACM Other conferences
ICCA '22: Proceedings of the 2nd International Conference on Computing Advancements
March 2022
543 pages
ISBN:9781450397346
DOI:10.1145/3542954
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 August 2022

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Author Tags

  1. Classification
  2. Histopathological Image
  3. Lung Cancer
  4. Machine Learning

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  • (2023)Deep Learning-Assisted Lung Cancer Diagnosis from Histopathology Images2023 IEEE 5th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS)10.1109/ECBIOS57802.2023.10218594(17-20)Online publication date: 2-Jun-2023

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