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The detection of B-cell acute lymphoblastic leukemia (B-ALL) plays a crucial role in ensuring timely and effective treatment for patients. Recent advancements in Convolutional Neural Networks (CNNs) and deep learning techniques have shown promise in automating the detection and diagnosis of B-ALL. This project provides a concise overview of the literature surrounding the use of CNN models and deep learning approaches for B-ALL detection. The studies reviewed demonstrate the effectiveness of these techniques in achieving high accuracy and improving the speed of diagnosis. The application of CNN models and deep learning in B-ALL detection has the potential to enhance early identification and improve patient outcomes.

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Detection of B-ALL Using CNN Model and Deep Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Shital Dongre,
  • Yash Chindhe,
  • Mayur Dabade,
  • Savani Bondre,
  • Anannya Chaudhary

摘要

The detection of B-cell acute lymphoblastic leukemia (B-ALL) plays a crucial role in ensuring timely and effective treatment for patients. Recent advancements in Convolutional Neural Networks (CNNs) and deep learning techniques have shown promise in automating the detection and diagnosis of B-ALL. This project provides a concise overview of the literature surrounding the use of CNN models and deep learning approaches for B-ALL detection. The studies reviewed demonstrate the effectiveness of these techniques in achieving high accuracy and improving the speed of diagnosis. The application of CNN models and deep learning in B-ALL detection has the potential to enhance early identification and improve patient outcomes.