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Related Experiment Video

Updated: May 6, 2026

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

11.5K

Multi-task advanced convolutional neural network for robust lymphoblastic leukemia diagnosis, classification, and

Sercan Yalcin1, Zuhal Cetin Yalcin2, Muhammed Yildirim3

  • 1Computer Engineering, Adiyaman University, Adiyaman, Turkey.

Peerj. Computer Science
|September 24, 2025
PubMed
Summary

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Classification of Leukocytes01:30

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Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
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This summary is machine-generated.

A novel multi-task advanced convolutional neural network (MTA-CNN) accurately detects Acute Lymphoblastic Leukemia (ALL) in medical images. This deep learning approach improves diagnostic efficiency and accuracy for this hematologic malignancy.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Hematology

Background:

  • Acute Lymphoblastic Leukemia (ALL) is a critical hematologic malignancy requiring precise and prompt diagnosis for effective patient management.
  • Current diagnostic methods for ALL can be time-consuming and may benefit from advanced computational approaches.

Purpose of the Study:

  • To introduce and evaluate a novel Multi-Task Advanced Convolutional Neural Network (MTA-CNN) for the simultaneous detection and classification of ALL in medical imaging data.
  • To assess the MTA-CNN's performance in improving diagnostic accuracy, efficiency, and localization of relevant features for ALL.

Main Methods:

  • Development of a deep learning framework, MTA-CNN, utilizing Convolutional Neural Networks (CNNs) for feature extraction from medical images.
  • Implementation of a multi-task learning strategy encompassing expression classification and disease detection to enhance feature generalizability.
Keywords:
Artificial intelligenceConvolutional neural networksDeep learningLymphoblastic leukemia

Related Experiment Videos

Last Updated: May 6, 2026

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

11.5K
  • Application of non-maximum suppression for refining detection results and analysis of facial landmark localization for identifying ALL-associated abnormalities.

    • Main Results:

    • The MTA-CNN achieved high performance metrics, including an accuracy of 0.978, precision of 0.979, recall of 0.967, and an F1-score of 0.973.
    • The model demonstrated superior performance compared to baseline methods, with a specificity of 0.991 and a Negative Predictive Value (NPV) of 0.990.
    • Accurate localization of key facial landmarks was achieved, providing valuable insights for further analysis of ALL-related changes.

    Conclusions:

    • The MTA-CNN framework presents a robust and accurate method for the detection and classification of Acute Lymphoblastic Leukemia in medical imaging.
    • The multi-task learning approach and cascaded CNN structure contribute to improved feature learning and diagnostic performance.
    • This novel framework shows significant potential for enhancing the efficiency and accuracy of ALL diagnosis in clinical settings.