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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
INTI International University
2024
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| Subjects: | |
| Online Access: | http://eprints.intimal.edu.my/2054/ http://eprints.intimal.edu.my/2054/1/jods2024_55.pdf http://eprints.intimal.edu.my/2054/2/595 |
| _version_ | 1848766907929329664 |
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| 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 |
| building | INTI Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-14T11:58:37Z |
| format | Article |
| id | intimal-2054 |
| institution | INTI International University |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T11:58:37Z |
| publishDate | 2024 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-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 |
| 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 |
| title | 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_short | Comparison of ELM, LSTM, and CNN Models in Breast Cancer
Classification |
| title_sort | comparison of elm, lstm, and cnn models in breast cancer
classification |
| topic | QA75 Electronic computers. Computer science QA76 Computer software RC0254 Neoplasms. Tumors. Oncology (including Cancer) T Technology (General) |
| url | http://eprints.intimal.edu.my/2054/ http://eprints.intimal.edu.my/2054/ http://eprints.intimal.edu.my/2054/1/jods2024_55.pdf http://eprints.intimal.edu.my/2054/2/595 |