Sağlıkta Görüntü İşleme ve Derin Öğrenme: Klinik Karar Destek Sistemleri İçin Yöntemler ve Uygulamalar
Özet
Referanslar
Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Medical Image Analysis. 2017;42:60–88. doi:10.1016/j.media.2017.07.005
Razzak MI, Naz S, Zaib A. Deep learning for medical image processing: Overview, challenges and the future. In: Dey N, Ashour AS, Borra S (eds.). Classification in BioApps: Automation of Decision Making. Cham: Springer; 2017. p. 323–350. doi:10.1007/978-3-319-65981-7_12
Yılmaz IN, Küçük IF, Yıldız IE, et al. Asistan hekimlerin zihinsel yorgunluk deneyimleri üzerine nitel bir araştırma. Nobel Medicus Journal. 2024;20(1).
Bi WL, Hosny A, Schabath MB, et al. Artificial intelligence in cancer imaging: Clinical challenges and applications. CA: A Cancer Journal for Clinicians. 2019;69(2):127–157. doi:10.3322/caac.21552
Mamdouh D, Attia M, Osama M, et al. Advancements in radiology report generation: A comprehensive analysis. Bioengineering. 2025;12(7):693. doi:10.3390/bioengineering12070693
Pacal I, Celik O, Bayram B, et al. Enhancing EfficientNetv2 with global and efficient channel attention mechanisms for accurate MRI-based brain tumor classification. Cluster Computing. 2024;27(8):11187–11212. doi:10.1007/s10586-024-04532-1
Ibtehaz N, Rahman MS. MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural Networks. 2020;121:74–87. doi:10.1016/j.neunet.2019.08.025
Çelik Ö, Tüfekci N. Use of deep learning in clinical decision support systems in healthcare. Journal of Current Researches on Health Sector. 2025;15(1):19–32. doi:10.26579/jocrehes.15.1.2
Deheyab AOA, Alwan MH, Rezzaqe IKA, et al. An overview of challenges in medical image processing. In: Proceedings of the 6th International Conference on Future Networks & Distributed Systems. 2022, New York. p. 511–516.. doi:10.1145/3584202.3584278
Goel N, Yadav A, Singh BM. Medical image processing: A review. In: Second International Innovative Applications of Computational Intelligence on Power, Energy and Controls with their Impact on Humanity (CIPECH). 2016, Ghaziabad, India. p. 57–62. Doi:10.1109/CIPECH.2016.7918737
Alnaggar OAMF, Jagadale BN, Saif MAN, et al. Efficient artificial intelligence approaches for medical image processing in healthcare: Comprehensive review, taxonomy, and analysis. Artificial Intelligence Review. 2024;57(8):221. doi:10.1007/s10462-024-10814-2
Ramesh KKD, Kumar GK, Swapna K, et al. A review of medical image segmentation algorithms. EAI Endorsed Transactions on Pervasive Health and Technology. 2021;7(27). doi:10.4108/eai.12-4-2021.169184
Deserno TM. Fundamentals of biomedical image processing. In: Deserno TM (ed.). Biomedical Image Processing. Berlin, Heidelberg: Springer; 2010. p. 1–51.
Wang J, Zhu H, Wang SH, et al. A review of deep learning on medical image analysis. Mobile Networks and Applications. 2021;26(1):351–380. doi:10.1007/s11036-020-01672-7
Anwar SM, Majid M, Qayyum A, et al. Medical image analysis using convolutional neural networks: A review. Journal of Medical Systems. 2018;42(11):226. doi:10.1007/s10916-018-1088-1
Matsuzaka Y, Iyoda M. Applications, image analysis, and interpretation of computer vision in medical imaging. Frontiers in Radiology. 2025;5:1733003. doi:10.3389/fradi.2025.1733003
Rahman A, Debnath T, Kundu D, et al. Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities. AIMS Public Health. 2024;11(1):58. doi:10.3934/publichealth.2024004
Das S, Nayak SP, Sahoo B, et al. Machine learning in healthcare analytics: A state-of-the-art review. Archives of Computational Methods in Engineering. 2024;31(7):3923–3962. doi:10.1007/s11831-024-10098-3
Bolhasani H, Mohseni M, Rahmani AM. Deep learning applications for IoT in health care: A systematic review. Informatics in Medicine Unlocked. 2021;23:100550. doi:10.1016/j.imu.2021.100550
Abdel-Jaber H, Devassy D, Al Salam A, et al. A review of deep learning algorithms and their applications in healthcare. Algorithms. 2022;15(2):71. doi:10.3390/a15020071
Esteva A, Robicquet A, Ramsundar B, et al. A guide to deep learning in healthcare. Nature Medicine. 2019;25(1):24–29. doi:10.1038/s41591-018-0316-z
Singh SP, Wang L, Gupta S, et al. 3D deep learning on medical images: a review. Sensors. 2020;20(18):5097. doi:10.3390/s20185097
Hasanah SA, Pravitasari AA, Abdullah AS, et al. A deep learning review of ResNet architecture for lung disease identification in CXR image. Applied Sciences. 2023;13(24):13111. doi:10.3390/app132413111
Zubair Rahman AM, Mythili R, Chokkanathan K, et al. Enhancing image-based diagnosis of gastrointestinal tract diseases through deep learning with EfficientNet and advanced data augmentation techniques. BMC Medical Imaging. 2024;24(1):306. doi:10.1186/s12880-024-01479-y
Zhang H, Qie Y. Applying deep learning to medical imaging: a review. Applied Sciences. 2023;13(18):10521. doi:10.3390/app131810521
Abdou MA. Literature review: Efficient deep neural networks techniques for medical image analysis. Neural Computing and Applications. 2022;34(8):5791–5812. doi:10.1007/s00521-022-06960-9
Yousef R, Gupta G, Yousef N, et al. A holistic overview of deep learning approach in medical imaging. Multimedia Systems. 2022;28(3):881–914. doi:10.1007/s00530-021-00884-5
Cong L, Feng W, Yao Z, et al. Deep learning model as a new trend in computer-aided diagnosis of tumor pathology for lung cancer. Journal of Cancer. 2020;11(12):3615. doi:10.7150/jca.43268
Sutton RT, Pincock D, Baumgart DC, et al. An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digital Medicine. 2020;3(1):17. doi:10.1038/s41746-020-0221-y
Antoniadi AM, Du Y, Guendouz Y, et al. Current challenges and future opportunities for XAI in machine learning-based clinical decision support systems: a systematic review. Applied Sciences. 2021;11(11):5088. doi:10.3390/app11115088
Mukherjee T, Pournik O, Lim Choi Keung SN, et al. Clinical decision support systems for brain tumour diagnosis and prognosis: A systematic review. Cancers. 2023;15(13):3523. doi:10.3390/cancers15133523
Ogut E. Artificial intelligence in clinical medicine: challenges across diagnostic imaging, clinical decision support, surgery, pathology, and drug discovery. Clinics and Practice. 2025;15(9):169. doi:10.3390/clinpract15090169
Shaikh F, Dehmeshki J, Bisdas S, et al. Artificial intelligence-based clinical decision support systems using advanced medical imaging and radiomics. Current Problems in Diagnostic Radiology. 2021;50(2):262–267. doi: 10.1067/j.cpradiol.2020.05.006
Tsolaki E, Svolos P, Kousi E, et al. Fast spectroscopic multiple analysis (FASMA) for brain tumor classification: a clinical decision support system utilizing multi-parametric 3T MR data. International Journal of Computer Assisted Radiology and Surgery. 2015;10(7):1149–1166. doi:10.1007/s11548-014-1088-7
Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402–2410. doi:10.1001/jama.2016.17216
Rodriguez-Ruiz A, Lång K, Gubern-Merida A, et al. Stand-alone artificial intelligence for breast cancer detection in mammography: comparison with 101 radiologists. JNCI: Journal of the National Cancer Institute. 2019;111(9):916–922. doi:10.1093/jnci/djy222
Zare S, Mobarak Z, Meidani Z, et al. Effectiveness of clinical decision support systems on the appropriate use of imaging for central nervous system injuries: a systematic review. Applied Clinical Informatics. 2022;13(1):37–52. doi:10.1055/s-0041-1740921
Dijk SW, Wollny C, Barkhausen J, et al. Evaluation of a clinical decision support system for imaging requests: a cluster randomized clinical trial. JAMA. 2025;333(14):1212–1221.doi: 10.1001/jama.2024.27853
Erukude ST, Marella VC, Veluru SR. Explainable deep learning in medical imaging: Brain tumor and pneumonia detection. In: Proceedings of the 4th International Conference on Innovative Mechanisms for Industry Applications (ICIMIA). 2025. doi: 10.1109/ICIMIA67127.2025.11200629.
Houssein EH, Gamal AM, Younis EM, et al. Explainable artificial intelligence for medical imaging systems using deep learning: a comprehensive review. Cluster Computing. 2025;28(7):469. doi: 10.1007/s10586-025-05281-5
Cui L, Fan Z, Yang Y, et al. Deep learning in ischemic stroke imaging analysis: a comprehensive review. BioMed Research International. 2022;2022(1):2456550. doi: 10.1155/2022/2456550
Issaiy M, Zarei D, Kolahi S, et al. Machine learning and deep learning algorithms in stroke medicine: a systematic review of hemorrhagic transformation prediction models. Journal of Neurology. 2025;272(1):37. doi: 10.1007/s00415-024-12810-6
Zhou SK, Greenspan H, Davatzikos C, et al. A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises. Proceedings of the IEEE. 2021;109(5):820–838.. doi: 10.1109/JPROC.2021.3054390
Ihongbe E, Fouad S, Mahmoud TF, et al. Evaluating explainable artificial intelligence (XAI) techniques in chest radiology imaging through a human-centered lens. PLoS One. 2024;19(10):e0308758. doi: 10.1371/journal.pone.0308758
Muchuchuti S, Viriri S. Retinal disease detection using deep learning techniques: a comprehensive review. Journal of Imaging. 2023;9(4):84. doi: 10.3390/jimaging9040084
Suara S, Jha A, Sinha P, et al. Is Grad-CAM explainable in medical images? In: International Conference on Computer Vision and Image Processing. 2023.
Attri N, Mahajan S. Brain tumor detection with explainable AI: A Grad-CAM approach to medical imaging. In: International Conference on Electronics, AI and Computing (EAIC). 2025. doi: 10.1109/EAIC66483.2025.11101626.
Zhang H, Ogasawara K. Grad-CAM-based explainable artificial intelligence related to medical text processing. Bioengineering. 2023;10(9):1070. doi: 10.3390/bioengineering10091070
Ray PP. A review on explainable artificial intelligence in radiomics: State-of-the-art tools, prospective use cases, challenges and future directions. European Journal of Radiology Artificial Intelligence. 2025:100069. doi: 10.1016/j.ejrai.2025.100069
Ahmed F, Naz NS, Khan S, et al. Explainable artificial intelligence (XAI) in medical imaging: a systematic review of techniques, applications, and challenges. BMC Medical Imaging. 2026. doi: 10.1186/s12880-025-02118-w
Brima Y, Atemkeng M. Robustness and scalability of machine learning for imbalanced clinical data in emergency and critical care. arXiv Preprint. 2025. doi: 10.48550/arXiv.2512.21602
Agyemang EF, Mensah JA, Nyarko E, et al. Addressing class imbalance problem in health data classification: Practical application from an oversampling viewpoint. Applied Computational Intelligence and Soft Computing. 2025;2025(1):1013769. doi: 10.1155/acis/1013769
Garcea F, Serra A, Lamberti F, et al. Data augmentation for medical imaging: A systematic literature review. Computers in Biology and Medicine. 2023;152:106391. doi: 10.1016/j.compbiomed.2022.106391
Alzubaidi L, Al-Amidie M, Al-Asadi A, et al. Novel transfer learning approach for medical imaging with limited labeled data. Cancers. 2021;13(7):1590. doi: 10.3390/cancers13071590
Yadav N, Pandey S, Gupta A, et al. Data privacy in healthcare: in the era of artificial intelligence. Indian Dermatology Online Journal. 2023;14(6):788–792. doi: 10.4103/idoj.idoj_543_23
Goceri E. Medical image data augmentation: techniques, comparisons and interpretations. Artificial Intelligence Review. 2023;56(11):12561–12605. doi: 10.1007/s10462-023-10453-z
Li M, Jiang Y, Zhang Y, et al. Medical image analysis using deep learning algorithms. Frontiers in Public Health. 2023;11:1273253. doi: 10.3389/fpubh.2023.1273253
Varoquaux G, Colliot O. Evaluating machine learning models and their diagnostic value. In: Colliot O, Varoquaux G (eds.). Machine Learning for Brain Disorders. London: Academic Press; 2023. p. 601–630. doi: 10.1007/978-1-0716-3195-9_20
Salehi AW, Khan S, Gupta G, et al. A study of CNN and transfer learning in medical imaging: Advantages, challenges, future scope. Sustainability. 2023;15(7):5930. doi: 10.3390/su15075930
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Referanslar
Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Medical Image Analysis. 2017;42:60–88. doi:10.1016/j.media.2017.07.005
Razzak MI, Naz S, Zaib A. Deep learning for medical image processing: Overview, challenges and the future. In: Dey N, Ashour AS, Borra S (eds.). Classification in BioApps: Automation of Decision Making. Cham: Springer; 2017. p. 323–350. doi:10.1007/978-3-319-65981-7_12
Yılmaz IN, Küçük IF, Yıldız IE, et al. Asistan hekimlerin zihinsel yorgunluk deneyimleri üzerine nitel bir araştırma. Nobel Medicus Journal. 2024;20(1).
Bi WL, Hosny A, Schabath MB, et al. Artificial intelligence in cancer imaging: Clinical challenges and applications. CA: A Cancer Journal for Clinicians. 2019;69(2):127–157. doi:10.3322/caac.21552
Mamdouh D, Attia M, Osama M, et al. Advancements in radiology report generation: A comprehensive analysis. Bioengineering. 2025;12(7):693. doi:10.3390/bioengineering12070693
Pacal I, Celik O, Bayram B, et al. Enhancing EfficientNetv2 with global and efficient channel attention mechanisms for accurate MRI-based brain tumor classification. Cluster Computing. 2024;27(8):11187–11212. doi:10.1007/s10586-024-04532-1
Ibtehaz N, Rahman MS. MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural Networks. 2020;121:74–87. doi:10.1016/j.neunet.2019.08.025
Çelik Ö, Tüfekci N. Use of deep learning in clinical decision support systems in healthcare. Journal of Current Researches on Health Sector. 2025;15(1):19–32. doi:10.26579/jocrehes.15.1.2
Deheyab AOA, Alwan MH, Rezzaqe IKA, et al. An overview of challenges in medical image processing. In: Proceedings of the 6th International Conference on Future Networks & Distributed Systems. 2022, New York. p. 511–516.. doi:10.1145/3584202.3584278
Goel N, Yadav A, Singh BM. Medical image processing: A review. In: Second International Innovative Applications of Computational Intelligence on Power, Energy and Controls with their Impact on Humanity (CIPECH). 2016, Ghaziabad, India. p. 57–62. Doi:10.1109/CIPECH.2016.7918737
Alnaggar OAMF, Jagadale BN, Saif MAN, et al. Efficient artificial intelligence approaches for medical image processing in healthcare: Comprehensive review, taxonomy, and analysis. Artificial Intelligence Review. 2024;57(8):221. doi:10.1007/s10462-024-10814-2
Ramesh KKD, Kumar GK, Swapna K, et al. A review of medical image segmentation algorithms. EAI Endorsed Transactions on Pervasive Health and Technology. 2021;7(27). doi:10.4108/eai.12-4-2021.169184
Deserno TM. Fundamentals of biomedical image processing. In: Deserno TM (ed.). Biomedical Image Processing. Berlin, Heidelberg: Springer; 2010. p. 1–51.
Wang J, Zhu H, Wang SH, et al. A review of deep learning on medical image analysis. Mobile Networks and Applications. 2021;26(1):351–380. doi:10.1007/s11036-020-01672-7
Anwar SM, Majid M, Qayyum A, et al. Medical image analysis using convolutional neural networks: A review. Journal of Medical Systems. 2018;42(11):226. doi:10.1007/s10916-018-1088-1
Matsuzaka Y, Iyoda M. Applications, image analysis, and interpretation of computer vision in medical imaging. Frontiers in Radiology. 2025;5:1733003. doi:10.3389/fradi.2025.1733003
Rahman A, Debnath T, Kundu D, et al. Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities. AIMS Public Health. 2024;11(1):58. doi:10.3934/publichealth.2024004
Das S, Nayak SP, Sahoo B, et al. Machine learning in healthcare analytics: A state-of-the-art review. Archives of Computational Methods in Engineering. 2024;31(7):3923–3962. doi:10.1007/s11831-024-10098-3
Bolhasani H, Mohseni M, Rahmani AM. Deep learning applications for IoT in health care: A systematic review. Informatics in Medicine Unlocked. 2021;23:100550. doi:10.1016/j.imu.2021.100550
Abdel-Jaber H, Devassy D, Al Salam A, et al. A review of deep learning algorithms and their applications in healthcare. Algorithms. 2022;15(2):71. doi:10.3390/a15020071
Esteva A, Robicquet A, Ramsundar B, et al. A guide to deep learning in healthcare. Nature Medicine. 2019;25(1):24–29. doi:10.1038/s41591-018-0316-z
Singh SP, Wang L, Gupta S, et al. 3D deep learning on medical images: a review. Sensors. 2020;20(18):5097. doi:10.3390/s20185097
Hasanah SA, Pravitasari AA, Abdullah AS, et al. A deep learning review of ResNet architecture for lung disease identification in CXR image. Applied Sciences. 2023;13(24):13111. doi:10.3390/app132413111
Zubair Rahman AM, Mythili R, Chokkanathan K, et al. Enhancing image-based diagnosis of gastrointestinal tract diseases through deep learning with EfficientNet and advanced data augmentation techniques. BMC Medical Imaging. 2024;24(1):306. doi:10.1186/s12880-024-01479-y
Zhang H, Qie Y. Applying deep learning to medical imaging: a review. Applied Sciences. 2023;13(18):10521. doi:10.3390/app131810521
Abdou MA. Literature review: Efficient deep neural networks techniques for medical image analysis. Neural Computing and Applications. 2022;34(8):5791–5812. doi:10.1007/s00521-022-06960-9
Yousef R, Gupta G, Yousef N, et al. A holistic overview of deep learning approach in medical imaging. Multimedia Systems. 2022;28(3):881–914. doi:10.1007/s00530-021-00884-5
Cong L, Feng W, Yao Z, et al. Deep learning model as a new trend in computer-aided diagnosis of tumor pathology for lung cancer. Journal of Cancer. 2020;11(12):3615. doi:10.7150/jca.43268
Sutton RT, Pincock D, Baumgart DC, et al. An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digital Medicine. 2020;3(1):17. doi:10.1038/s41746-020-0221-y
Antoniadi AM, Du Y, Guendouz Y, et al. Current challenges and future opportunities for XAI in machine learning-based clinical decision support systems: a systematic review. Applied Sciences. 2021;11(11):5088. doi:10.3390/app11115088
Mukherjee T, Pournik O, Lim Choi Keung SN, et al. Clinical decision support systems for brain tumour diagnosis and prognosis: A systematic review. Cancers. 2023;15(13):3523. doi:10.3390/cancers15133523
Ogut E. Artificial intelligence in clinical medicine: challenges across diagnostic imaging, clinical decision support, surgery, pathology, and drug discovery. Clinics and Practice. 2025;15(9):169. doi:10.3390/clinpract15090169
Shaikh F, Dehmeshki J, Bisdas S, et al. Artificial intelligence-based clinical decision support systems using advanced medical imaging and radiomics. Current Problems in Diagnostic Radiology. 2021;50(2):262–267. doi: 10.1067/j.cpradiol.2020.05.006
Tsolaki E, Svolos P, Kousi E, et al. Fast spectroscopic multiple analysis (FASMA) for brain tumor classification: a clinical decision support system utilizing multi-parametric 3T MR data. International Journal of Computer Assisted Radiology and Surgery. 2015;10(7):1149–1166. doi:10.1007/s11548-014-1088-7
Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402–2410. doi:10.1001/jama.2016.17216
Rodriguez-Ruiz A, Lång K, Gubern-Merida A, et al. Stand-alone artificial intelligence for breast cancer detection in mammography: comparison with 101 radiologists. JNCI: Journal of the National Cancer Institute. 2019;111(9):916–922. doi:10.1093/jnci/djy222
Zare S, Mobarak Z, Meidani Z, et al. Effectiveness of clinical decision support systems on the appropriate use of imaging for central nervous system injuries: a systematic review. Applied Clinical Informatics. 2022;13(1):37–52. doi:10.1055/s-0041-1740921
Dijk SW, Wollny C, Barkhausen J, et al. Evaluation of a clinical decision support system for imaging requests: a cluster randomized clinical trial. JAMA. 2025;333(14):1212–1221.doi: 10.1001/jama.2024.27853
Erukude ST, Marella VC, Veluru SR. Explainable deep learning in medical imaging: Brain tumor and pneumonia detection. In: Proceedings of the 4th International Conference on Innovative Mechanisms for Industry Applications (ICIMIA). 2025. doi: 10.1109/ICIMIA67127.2025.11200629.
Houssein EH, Gamal AM, Younis EM, et al. Explainable artificial intelligence for medical imaging systems using deep learning: a comprehensive review. Cluster Computing. 2025;28(7):469. doi: 10.1007/s10586-025-05281-5
Cui L, Fan Z, Yang Y, et al. Deep learning in ischemic stroke imaging analysis: a comprehensive review. BioMed Research International. 2022;2022(1):2456550. doi: 10.1155/2022/2456550
Issaiy M, Zarei D, Kolahi S, et al. Machine learning and deep learning algorithms in stroke medicine: a systematic review of hemorrhagic transformation prediction models. Journal of Neurology. 2025;272(1):37. doi: 10.1007/s00415-024-12810-6
Zhou SK, Greenspan H, Davatzikos C, et al. A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises. Proceedings of the IEEE. 2021;109(5):820–838.. doi: 10.1109/JPROC.2021.3054390
Ihongbe E, Fouad S, Mahmoud TF, et al. Evaluating explainable artificial intelligence (XAI) techniques in chest radiology imaging through a human-centered lens. PLoS One. 2024;19(10):e0308758. doi: 10.1371/journal.pone.0308758
Muchuchuti S, Viriri S. Retinal disease detection using deep learning techniques: a comprehensive review. Journal of Imaging. 2023;9(4):84. doi: 10.3390/jimaging9040084
Suara S, Jha A, Sinha P, et al. Is Grad-CAM explainable in medical images? In: International Conference on Computer Vision and Image Processing. 2023.
Attri N, Mahajan S. Brain tumor detection with explainable AI: A Grad-CAM approach to medical imaging. In: International Conference on Electronics, AI and Computing (EAIC). 2025. doi: 10.1109/EAIC66483.2025.11101626.
Zhang H, Ogasawara K. Grad-CAM-based explainable artificial intelligence related to medical text processing. Bioengineering. 2023;10(9):1070. doi: 10.3390/bioengineering10091070
Ray PP. A review on explainable artificial intelligence in radiomics: State-of-the-art tools, prospective use cases, challenges and future directions. European Journal of Radiology Artificial Intelligence. 2025:100069. doi: 10.1016/j.ejrai.2025.100069
Ahmed F, Naz NS, Khan S, et al. Explainable artificial intelligence (XAI) in medical imaging: a systematic review of techniques, applications, and challenges. BMC Medical Imaging. 2026. doi: 10.1186/s12880-025-02118-w
Brima Y, Atemkeng M. Robustness and scalability of machine learning for imbalanced clinical data in emergency and critical care. arXiv Preprint. 2025. doi: 10.48550/arXiv.2512.21602
Agyemang EF, Mensah JA, Nyarko E, et al. Addressing class imbalance problem in health data classification: Practical application from an oversampling viewpoint. Applied Computational Intelligence and Soft Computing. 2025;2025(1):1013769. doi: 10.1155/acis/1013769
Garcea F, Serra A, Lamberti F, et al. Data augmentation for medical imaging: A systematic literature review. Computers in Biology and Medicine. 2023;152:106391. doi: 10.1016/j.compbiomed.2022.106391
Alzubaidi L, Al-Amidie M, Al-Asadi A, et al. Novel transfer learning approach for medical imaging with limited labeled data. Cancers. 2021;13(7):1590. doi: 10.3390/cancers13071590
Yadav N, Pandey S, Gupta A, et al. Data privacy in healthcare: in the era of artificial intelligence. Indian Dermatology Online Journal. 2023;14(6):788–792. doi: 10.4103/idoj.idoj_543_23
Goceri E. Medical image data augmentation: techniques, comparisons and interpretations. Artificial Intelligence Review. 2023;56(11):12561–12605. doi: 10.1007/s10462-023-10453-z
Li M, Jiang Y, Zhang Y, et al. Medical image analysis using deep learning algorithms. Frontiers in Public Health. 2023;11:1273253. doi: 10.3389/fpubh.2023.1273253
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