Fossil_Net Lung Cancer Prediction and Classification from CT Images Using Convolution Neural Networks

Lung cancer is the leading cause of death worldwide for both men and women. The most well-known disease is the heart disease followed by lung cancer. Early discovery of lung cancer can effectively treat lung cancer at an early stage. The potential for an early diagnosis of lung cancer to increase a patient's chance of survival. goal of this work was to identify lung malignancies in their early stages using supervised Learning. The Random forest model outperforms all other supervised machine learning methods for the Prediction of Lung cancer. The Objective of this study is to compare the different types of the machine Learning algorithms and provide the better Accuracy.

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Publication Details

Published
2024-11-12
DOI
https://doi.org/10.1109/iccda64887.2024.10867395
Citations
1
Primary Topic
Radiomics and Machine Learning in Medical Imaging
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article
Field-Weighted Citation Impact
0.40
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article

Fossil_Net Lung Cancer Prediction and Classification from CT Images Using Convolution Neural Networks

Eduardo F. Morales, V. Deepa
1 citations
Radiomics and Machine Learning in Medical Imaging
0.40
article

Fossil_Net Lung Cancer Prediction and Classification from CT Images Using Convolution Neural Networks

Eduardo F. Morales, V. Deepa
article en
1 citations

Abstract

Lung cancer is the leading cause of death worldwide for both men and women. The most well-known disease is the heart disease followed by lung cancer. Early discovery of lung cancer can effectively treat lung cancer at an early stage. The potential for an early diagnosis of lung cancer to increase a patient's chance of survival. goal of this work was to identify lung malignancies in their early stages using supervised Learning. The Random forest model outperforms all other supervised machine learning methods for the Prediction of Lung cancer. The Objective of this study is to compare the different types of the machine Learning algorithms and provide the better Accuracy.

Good health and well-being
Openalex Percentile: Top 31%
Radiomics and Machine Learning in Medical Imaging
0.40
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