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Next-Generation AI Frameworks for Leukemia Detection: Techniques, Dataset, Challenges, and Future Research Horizons.

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Title: Next-Generation AI Frameworks for Leukemia Detection: Techniques, Dataset, Challenges, and Future Research Horizons.
Authors: Kumar, Krishna1 (AUTHOR) krishnamathsforyou@gmail.com, Verma, Vivek Singh1 (AUTHOR) vivek.v@hbtu.ac.in
Source: Archives of Computational Methods in Engineering. Jun2026, Vol. 33 Issue 5, p7031-7057. 27p.
Subjects: Artificial intelligence, Deep learning, Diagnostic imaging, Knowledge transfer, Data quality, Machine learning, Cancer diagnosis
Abstract: Leukemia is a serious malignancy of the blood and bone marrow characterized by abnormal proliferation of white blood cells, making early diagnosis essential. Conventional diagnostic methods, such as bone marrow biopsy and complete blood count analysis, are time-consuming, subjective, and dependent on expert interpretation. Recent advances in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), have enabled automated leukemia detection using peripheral blood smear and bone marrow smear images. However, challenges such as limited annotated datasets, class imbalance, and overfitting in deep models persist. This paper categorizes existing approaches into ML-based, DL-based, and hybrid or ensemble frameworks and reviews their algorithms, datasets, and evaluation metrics. CNN-based transfer learning models, notably ResNet and DenseNet, show strong feature extraction capability, while hybrid DL–ML approaches help address data imbalance. The survey emphasizes the need for diverse datasets and integrated classification–segmentation frameworks to improve robustness and clinical applicability. [ABSTRACT FROM AUTHOR]
Copyright of Archives of Computational Methods in Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Leukemia is a serious malignancy of the blood and bone marrow characterized by abnormal proliferation of white blood cells, making early diagnosis essential. Conventional diagnostic methods, such as bone marrow biopsy and complete blood count analysis, are time-consuming, subjective, and dependent on expert interpretation. Recent advances in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), have enabled automated leukemia detection using peripheral blood smear and bone marrow smear images. However, challenges such as limited annotated datasets, class imbalance, and overfitting in deep models persist. This paper categorizes existing approaches into ML-based, DL-based, and hybrid or ensemble frameworks and reviews their algorithms, datasets, and evaluation metrics. CNN-based transfer learning models, notably ResNet and DenseNet, show strong feature extraction capability, while hybrid DL–ML approaches help address data imbalance. The survey emphasizes the need for diverse datasets and integrated classification–segmentation frameworks to improve robustness and clinical applicability. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Archives of Computational Methods in Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Type: general
      – SubjectFull: Deep learning
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      – SubjectFull: Diagnostic imaging
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      – SubjectFull: Data quality
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              Text: Jun2026
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