An integrated framework based deep learning for cancer classification using microarray datasets.
Around the world, cancer is one of the leading reasons of mortality. The importance of earlier detection and prognosis of cancer types is highly significant for patients’ health. In recent research, deep neural networks were trained using gene expression microarray, to classify cancer. Biologists ar...
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| Format: | Article |
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Springer Science and Business Media Deutschland GmbH
2023
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| author | Alrefai, Nashat Ibrahim, Othman Shehzad, Hafiz Muhammad Faisal Altigani, Abdelrahman Abu-ulbeh, Waheeb Alzaqebah, Malek Alsmadi, Mutasem K. |
| author_facet | Alrefai, Nashat Ibrahim, Othman Shehzad, Hafiz Muhammad Faisal Altigani, Abdelrahman Abu-ulbeh, Waheeb Alzaqebah, Malek Alsmadi, Mutasem K. |
| author_sort | Alrefai, Nashat |
| collection | ePrints |
| description | Around the world, cancer is one of the leading reasons of mortality. The importance of earlier detection and prognosis of cancer types is highly significant for patients’ health. In recent research, deep neural networks were trained using gene expression microarray, to classify cancer. Biologists are able to monitor thousands of genes in one experiment using microarray technology. Microarray datasets are considered high-dimensional data, as they are cluttered with irrelevant, redundant, and noisy genes that contribute insignificantly to classification. The most informative genes contributing to cancer classification have been identified using computational intelligence algorithms. In this paper, we propose an integrated framework for cancer classification. This framework is divided into three tasks. Firstly, particle swarm optimization with ensemble learning (PSO-ensemble) reduces the microarray dataset's high dimensionality. Secondly, The Adaptive self-training method (ASTM) is used to solve low-size issues. Finally, a Convolutional Neural Network (CNN) was employed for classification. CNN has the ability to discover the complex non-linear relationships between features and select the most informative. Transfer learning was used sequentially with CNN to integrate the classification procedure because it can reduce the training time and computational complexity. Six microarray datasets are used, namely liver, breast, colon, prostate, central nervous system, and lung. The proposed CNN architecture with transfer learning provided 100% classification accuracy for colon, prostate, CNS and lung microarray datasets, and 97.62%, 95.45% accuracy for liver and breast cancer respectively. Experiments show that our proposed method delivers the highest classification accuracy and reduces training time with the smallest gene subset. |
| first_indexed | 2024-09-24T00:01:04Z |
| format | Article |
| id | utm.eprints-106234 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-09-24T00:01:04Z |
| publishDate | 2023 |
| publisher | Springer Science and Business Media Deutschland GmbH |
| record_format | dspace |
| spelling | utm.eprints-1062342024-06-20T02:14:04Z http://eprints.utm.my/106234/ An integrated framework based deep learning for cancer classification using microarray datasets. Alrefai, Nashat Ibrahim, Othman Shehzad, Hafiz Muhammad Faisal Altigani, Abdelrahman Abu-ulbeh, Waheeb Alzaqebah, Malek Alsmadi, Mutasem K. QA75 Electronic computers. Computer science Around the world, cancer is one of the leading reasons of mortality. The importance of earlier detection and prognosis of cancer types is highly significant for patients’ health. In recent research, deep neural networks were trained using gene expression microarray, to classify cancer. Biologists are able to monitor thousands of genes in one experiment using microarray technology. Microarray datasets are considered high-dimensional data, as they are cluttered with irrelevant, redundant, and noisy genes that contribute insignificantly to classification. The most informative genes contributing to cancer classification have been identified using computational intelligence algorithms. In this paper, we propose an integrated framework for cancer classification. This framework is divided into three tasks. Firstly, particle swarm optimization with ensemble learning (PSO-ensemble) reduces the microarray dataset's high dimensionality. Secondly, The Adaptive self-training method (ASTM) is used to solve low-size issues. Finally, a Convolutional Neural Network (CNN) was employed for classification. CNN has the ability to discover the complex non-linear relationships between features and select the most informative. Transfer learning was used sequentially with CNN to integrate the classification procedure because it can reduce the training time and computational complexity. Six microarray datasets are used, namely liver, breast, colon, prostate, central nervous system, and lung. The proposed CNN architecture with transfer learning provided 100% classification accuracy for colon, prostate, CNS and lung microarray datasets, and 97.62%, 95.45% accuracy for liver and breast cancer respectively. Experiments show that our proposed method delivers the highest classification accuracy and reduces training time with the smallest gene subset. Springer Science and Business Media Deutschland GmbH 2023-03 Article PeerReviewed Alrefai, Nashat and Ibrahim, Othman and Shehzad, Hafiz Muhammad Faisal and Altigani, Abdelrahman and Abu-ulbeh, Waheeb and Alzaqebah, Malek and Alsmadi, Mutasem K. (2023) An integrated framework based deep learning for cancer classification using microarray datasets. Journal of Ambient Intelligence and Humanized Computing, 14 (3). pp. 2249-2260. ISSN 1868-5137 http://dx.doi.org/10.1007/s12652-022-04482-9 DOI: 10.1007/s12652-022-04482-9 |
| spellingShingle | QA75 Electronic computers. Computer science Alrefai, Nashat Ibrahim, Othman Shehzad, Hafiz Muhammad Faisal Altigani, Abdelrahman Abu-ulbeh, Waheeb Alzaqebah, Malek Alsmadi, Mutasem K. An integrated framework based deep learning for cancer classification using microarray datasets. |
| title | An integrated framework based deep learning for cancer classification using microarray datasets. |
| title_full | An integrated framework based deep learning for cancer classification using microarray datasets. |
| title_fullStr | An integrated framework based deep learning for cancer classification using microarray datasets. |
| title_full_unstemmed | An integrated framework based deep learning for cancer classification using microarray datasets. |
| title_short | An integrated framework based deep learning for cancer classification using microarray datasets. |
| title_sort | integrated framework based deep learning for cancer classification using microarray datasets |
| topic | QA75 Electronic computers. Computer science |
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