Deep learning models for CT image classification: a comprehensive literature review Article Swipe
Isah Salim Ahmad
,
Jingjing Dai
,
Yaoqin Xie
,
Xiaokun Liang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.21037/qims-24-1400
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.21037/qims-24-1400
This review underscores the pivotal role of DL in advancing CT image analysis, particularly for COVID-19 and lung nodule detection. The integration of DL models into clinical workflows shows promising potential to enhance diagnostic accuracy and efficiency. However, challenges remain in areas of interpretability, validation, and regulatory compliance. The review advocates for continued research, interdisciplinary collaboration, and ethical considerations as DL technologies become integral to clinical practice. While traditional imaging techniques remain vital, the integration of DL represents a significant advancement in medical diagnostics, with far-reaching implications for future research, clinical practice, and healthcare policy.
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- Type
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https://doi.org/10.21037/qims-24-1400Digital Object Identifier
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Deep learning models for CT image classification: a comprehensive literature reviewWork title
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reviewOpenAlex work type
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enPrimary language
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2024Year of publication
- Publication date
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2024-12-31Full publication date if available
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Isah Salim Ahmad, Jingjing Dai, Yaoqin Xie, Xiaokun LiangList of authors in order
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https://doi.org/10.21037/qims-24-1400Publisher landing page
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://doi.org/10.21037/qims-24-1400Direct OA link when available
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Computer science, Artificial intelligence, Deep learning, Medical physics, Radiology, Pattern recognition (psychology), MedicineTop concepts (fields/topics) attached by OpenAlex
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9Total citation count in OpenAlex
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2025: 9Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.imaging | 69 |
| abstract_inverted_index.medical | 82 |
| abstract_inverted_index.pivotal | 4 |
| abstract_inverted_index.policy. | 94 |
| abstract_inverted_index.COVID-19 | 15 |
| abstract_inverted_index.However, | 37 |
| abstract_inverted_index.accuracy | 34 |
| abstract_inverted_index.clinical | 26, 65, 90 |
| abstract_inverted_index.integral | 63 |
| abstract_inverted_index.advancing | 9 |
| abstract_inverted_index.advocates | 50 |
| abstract_inverted_index.analysis, | 12 |
| abstract_inverted_index.continued | 52 |
| abstract_inverted_index.potential | 30 |
| abstract_inverted_index.practice, | 91 |
| abstract_inverted_index.practice. | 66 |
| abstract_inverted_index.promising | 29 |
| abstract_inverted_index.research, | 53, 89 |
| abstract_inverted_index.workflows | 27 |
| abstract_inverted_index.challenges | 38 |
| abstract_inverted_index.detection. | 19 |
| abstract_inverted_index.diagnostic | 33 |
| abstract_inverted_index.healthcare | 93 |
| abstract_inverted_index.regulatory | 46 |
| abstract_inverted_index.represents | 77 |
| abstract_inverted_index.techniques | 70 |
| abstract_inverted_index.advancement | 80 |
| abstract_inverted_index.compliance. | 47 |
| abstract_inverted_index.efficiency. | 36 |
| abstract_inverted_index.integration | 21, 74 |
| abstract_inverted_index.significant | 79 |
| abstract_inverted_index.traditional | 68 |
| abstract_inverted_index.underscores | 2 |
| abstract_inverted_index.validation, | 44 |
| abstract_inverted_index.diagnostics, | 83 |
| abstract_inverted_index.far-reaching | 85 |
| abstract_inverted_index.implications | 86 |
| abstract_inverted_index.particularly | 13 |
| abstract_inverted_index.technologies | 61 |
| abstract_inverted_index.collaboration, | 55 |
| abstract_inverted_index.considerations | 58 |
| abstract_inverted_index.interdisciplinary | 54 |
| abstract_inverted_index.interpretability, | 43 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.4699999988079071 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.95764963 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |