Optimized deep learning ensemble using Fast Osprey algorithm for accurate lymphoblastic leukemia detection - Takeaways - MDSpire

Optimized deep learning ensemble using Fast Osprey algorithm for accurate lymphoblastic leukemia detection

  • By

  • Narinder Kaur

  • Shakir Khan

  • Bobbinpreet Kaur

  • Amal Alomran

  • Sultan Ahmad

  • Thamer Alshammari

  • Fahad Omar Alomary

  • May 1, 2026

  • 0 min

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  • 1

    Acute Lymphoblastic Leukemia (ALL) is a serious blood malignancy requiring rapid and accurate diagnosis to improve survival rates.

  • 2

    The proposed FOO-Ensemble framework utilizes multiple CNNs and Fast Osprey Optimization to enhance diagnostic accuracy and efficiency.

  • 3

    The FOO-Ensemble framework achieved an accuracy of 97.76%, with high precision, recall, and F1-score, outperforming baseline models.

  • 4

    This ensemble approach reduces inference time compared to standalone models, enhancing computational efficiency in diagnostics.

  • 5

    The findings support the use of deep learning ensembles for reliable ALL detection, potentially improving clinical outcomes for patients.

Original Source(s)

Optimized deep learning ensemble using Fast Osprey algorithm for accurate lymphoblastic leukemia detection

  • by Narinder Kaur, Shakir Khan, Bobbinpreet Kaur, Amal Alomran, Sultan Ahmad, Thamer Alshammari, Fahad Omar Alomary

  • May 1, 2026

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