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An excessive production of undeveloped lymphocytes by the bone marrow is known as acute lymphoblastic leukemia (ALL). In the US, more than 6500 instances of ALL in adults and children are diagnosed each year; these cases make up about 25% of pediatric cancer cases, and the number is still rising. The development of AI and big data analytics has made it possible for doctors and radiologists to make better clinical decisions for early ALL diagnosis. This research presents an analysis of the performance of robust Swin Transformer model for cell image classification using medical data. The dataset utilized consisted of four classes: Early, Benign, Pre, and Pro. The performance is assessed using suitable measures, including recall, accuracy, F1-score, precision, training and validation loss, ROC curves, and confusion matrices.

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Acute Lymphoblastic Leukemia Subtypes Detection Using Swin Transformer Model

  • 会议论文

  • 发表日期：

    2025年

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  • Prakeerth Prasad,
  • L. Jani Anbarasi

摘要

An excessive production of undeveloped lymphocytes by the bone marrow is known as acute lymphoblastic leukemia (ALL). In the US, more than 6500 instances of ALL in adults and children are diagnosed each year; these cases make up about 25% of pediatric cancer cases, and the number is still rising. The development of AI and big data analytics has made it possible for doctors and radiologists to make better clinical decisions for early ALL diagnosis. This research presents an analysis of the performance of robust Swin Transformer model for cell image classification using medical data. The dataset utilized consisted of four classes: Early, Benign, Pre, and Pro. The performance is assessed using suitable measures, including recall, accuracy, F1-score, precision, training and validation loss, ROC curves, and confusion matrices.