Lung Cancer Diagnosis, Treatment, and Prognosis Using Machine Learning
Recent advances in imaging and sequencing technology have enabled a systematic advancement in the medical treatment of carcinoma of the lungs. Meanwhile, the human mind's ability to comprehend and make optimal use of the collection for this enormous amounts of knowledge is limited.. The integr...
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| Format: | Article |
| Language: | English |
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International Transactions on Electrical Engineering and Computer Science
2025-04-01
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| Series: | International Transactions on Electrical Engineering and Computer Science |
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| Online Access: | https://iteecs.com/index.php/iteecs/article/view/133 |
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| author | Rimjhim Kumari Shalu kumari Sharik Ahmad3 |
| author_facet | Rimjhim Kumari Shalu kumari Sharik Ahmad3 |
| author_sort | Rimjhim Kumari |
| collection | DOAJ |
| description |
Recent advances in imaging and sequencing technology have enabled a systematic advancement in the medical treatment of carcinoma of the lungs. Meanwhile, the human mind's ability to comprehend and make optimal use of the collection for this enormous amounts of knowledge is limited.. The integration and analysis of these vast and intricate datasets, which have thoroughly described lung cancer by utilizing various viewpoints from the accumulated data, are made possible in great part by machine learning-based methodologies. We give a summary of machine learning-based methods in this review that support the various facets of lung cancer diagnosis and treatment, such as immunotherapy practice, prognosis prediction, auxiliary diagnosis, and early detection. We also highlight the challenges and opportunities for additional artificial intelligence applications in lung disease.
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| format | Article |
| id | doaj-art-2626ef35dbf5497897828ea3f35e26c2 |
| institution | Kabale University |
| issn | 2583-6471 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | International Transactions on Electrical Engineering and Computer Science |
| record_format | Article |
| series | International Transactions on Electrical Engineering and Computer Science |
| spelling | doaj-art-2626ef35dbf5497897828ea3f35e26c22025-08-20T03:52:25ZengInternational Transactions on Electrical Engineering and Computer ScienceInternational Transactions on Electrical Engineering and Computer Science2583-64712025-04-014210.62760/iteecs.4.2.2025.133Lung Cancer Diagnosis, Treatment, and Prognosis Using Machine LearningRimjhim Kumari0https://orcid.org/0009-0009-6776-0411Shalu kumari1Sharik Ahmad32https://orcid.org/0000-0001-5994-4995Sharda UniversityDepartment of Computer Science & Applications, Sharda School of Computing Science & Engineering, Sharda University, Greater Noida – 201306, IndiaDepartment of Computer Science & Applications, Sharda School of Computing Science & Engineering, Sharda University, Greater Noida – 201306, India Recent advances in imaging and sequencing technology have enabled a systematic advancement in the medical treatment of carcinoma of the lungs. Meanwhile, the human mind's ability to comprehend and make optimal use of the collection for this enormous amounts of knowledge is limited.. The integration and analysis of these vast and intricate datasets, which have thoroughly described lung cancer by utilizing various viewpoints from the accumulated data, are made possible in great part by machine learning-based methodologies. We give a summary of machine learning-based methods in this review that support the various facets of lung cancer diagnosis and treatment, such as immunotherapy practice, prognosis prediction, auxiliary diagnosis, and early detection. We also highlight the challenges and opportunities for additional artificial intelligence applications in lung disease. https://iteecs.com/index.php/iteecs/article/view/133imaging and sequencing technologies, intricate datasets, immunotherapy, prediction, detection |
| spellingShingle | Rimjhim Kumari Shalu kumari Sharik Ahmad3 Lung Cancer Diagnosis, Treatment, and Prognosis Using Machine Learning International Transactions on Electrical Engineering and Computer Science imaging and sequencing technologies, intricate datasets, immunotherapy, prediction, detection |
| title | Lung Cancer Diagnosis, Treatment, and Prognosis Using Machine Learning |
| title_full | Lung Cancer Diagnosis, Treatment, and Prognosis Using Machine Learning |
| title_fullStr | Lung Cancer Diagnosis, Treatment, and Prognosis Using Machine Learning |
| title_full_unstemmed | Lung Cancer Diagnosis, Treatment, and Prognosis Using Machine Learning |
| title_short | Lung Cancer Diagnosis, Treatment, and Prognosis Using Machine Learning |
| title_sort | lung cancer diagnosis treatment and prognosis using machine learning |
| topic | imaging and sequencing technologies, intricate datasets, immunotherapy, prediction, detection |
| url | https://iteecs.com/index.php/iteecs/article/view/133 |
| work_keys_str_mv | AT rimjhimkumari lungcancerdiagnosistreatmentandprognosisusingmachinelearning AT shalukumari lungcancerdiagnosistreatmentandprognosisusingmachinelearning AT sharikahmad3 lungcancerdiagnosistreatmentandprognosisusingmachinelearning |