Comparison of ELM, LSTM, and CNN Models in Breast Cancer Classification

Classification can significantly impact treatment decisions and patient outcomes. This study evaluates and compares the performance of three machine learning models Extreme Learning Machine (ELM), Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN) in breast cancer classificati...

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Main Authors: Silvia, Ratna, M., Muflih, Haldi, Budiman, Usman, Syapotro, Muhammad, Hamdani
Format: Article
Language:English
English
Published: INTI International University 2024
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Online Access:http://eprints.intimal.edu.my/2054/1/jods2024_55.pdf
http://eprints.intimal.edu.my/2054/2/595
http://eprints.intimal.edu.my/2054/
http://ipublishing.intimal.edu.my/jods.html
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spelling my-inti-eprints.20542024-11-26T06:55:16Z http://eprints.intimal.edu.my/2054/ Comparison of ELM, LSTM, and CNN Models in Breast Cancer Classification Silvia, Ratna M., Muflih Haldi, Budiman Usman, Syapotro Muhammad, Hamdani QA75 Electronic computers. Computer science QA76 Computer software RC0254 Neoplasms. Tumors. Oncology (including Cancer) T Technology (General) Classification can significantly impact treatment decisions and patient outcomes. This study evaluates and compares the performance of three machine learning models Extreme Learning Machine (ELM), Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN) in breast cancer classification. ELM, known for its fast-learning speed and strong generalization, is compared with LSTM, which is effective in capturing long-term dependencies in sequential data, and CNN, which is renowned for its ability to automatically extract features from images and structured data. The models were trained and tested on a breast cancer dataset, focusing on accuracy and computational efficiency. The results revealed that while CNNs demonstrated better accuracy in feature-rich data, LSTMs excelled in handling sequential data patterns. On the other hand, ELM offers a good balance between training speed and classification performance. This comparative analysis provides valuable insights into the strengths and limitations of each model, contributing to the development of more effective breast cancer diagnostic tools. In this case, LSTM outperformed ELM by 0.91%, outperformed CNN significantly by 3.72%, and outperformed Improved LSTM by 0.91%. This indicate that the LSTM model shows higher accuracy in breast cancer classification. INTI International University 2024-11 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/2054/1/jods2024_55.pdf text en cc_by_4 http://eprints.intimal.edu.my/2054/2/595 Silvia, Ratna and M., Muflih and Haldi, Budiman and Usman, Syapotro and Muhammad, Hamdani (2024) Comparison of ELM, LSTM, and CNN Models in Breast Cancer Classification. Journal of Data Science, 2024 (55). pp. 1-5. ISSN 2805-5160 http://ipublishing.intimal.edu.my/jods.html
institution INTI International University
building INTI Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider INTI International University
content_source INTI Institutional Repository
url_provider http://eprints.intimal.edu.my
language English
English
topic QA75 Electronic computers. Computer science
QA76 Computer software
RC0254 Neoplasms. Tumors. Oncology (including Cancer)
T Technology (General)
spellingShingle QA75 Electronic computers. Computer science
QA76 Computer software
RC0254 Neoplasms. Tumors. Oncology (including Cancer)
T Technology (General)
Silvia, Ratna
M., Muflih
Haldi, Budiman
Usman, Syapotro
Muhammad, Hamdani
Comparison of ELM, LSTM, and CNN Models in Breast Cancer Classification
description Classification can significantly impact treatment decisions and patient outcomes. This study evaluates and compares the performance of three machine learning models Extreme Learning Machine (ELM), Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN) in breast cancer classification. ELM, known for its fast-learning speed and strong generalization, is compared with LSTM, which is effective in capturing long-term dependencies in sequential data, and CNN, which is renowned for its ability to automatically extract features from images and structured data. The models were trained and tested on a breast cancer dataset, focusing on accuracy and computational efficiency. The results revealed that while CNNs demonstrated better accuracy in feature-rich data, LSTMs excelled in handling sequential data patterns. On the other hand, ELM offers a good balance between training speed and classification performance. This comparative analysis provides valuable insights into the strengths and limitations of each model, contributing to the development of more effective breast cancer diagnostic tools. In this case, LSTM outperformed ELM by 0.91%, outperformed CNN significantly by 3.72%, and outperformed Improved LSTM by 0.91%. This indicate that the LSTM model shows higher accuracy in breast cancer classification.
format Article
author Silvia, Ratna
M., Muflih
Haldi, Budiman
Usman, Syapotro
Muhammad, Hamdani
author_facet Silvia, Ratna
M., Muflih
Haldi, Budiman
Usman, Syapotro
Muhammad, Hamdani
author_sort Silvia, Ratna
title Comparison of ELM, LSTM, and CNN Models in Breast Cancer Classification
title_short Comparison of ELM, LSTM, and CNN Models in Breast Cancer Classification
title_full Comparison of ELM, LSTM, and CNN Models in Breast Cancer Classification
title_fullStr Comparison of ELM, LSTM, and CNN Models in Breast Cancer Classification
title_full_unstemmed Comparison of ELM, LSTM, and CNN Models in Breast Cancer Classification
title_sort comparison of elm, lstm, and cnn models in breast cancer classification
publisher INTI International University
publishDate 2024
url http://eprints.intimal.edu.my/2054/1/jods2024_55.pdf
http://eprints.intimal.edu.my/2054/2/595
http://eprints.intimal.edu.my/2054/
http://ipublishing.intimal.edu.my/jods.html
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score 13.222552