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Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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ALL diagnosis: can efficiency and transparency coexist? An explainble deep learning approach.

Dost Muhammad1, Muhammad Salman2, Ayse Keles3

  • 1CRT-AI and ADAPT Research Centres, School of Computer Science, University of Galway, Galway, Ireland. d.muhammad1@universityofgalway.ie.

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|April 14, 2025
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Summary

This study presents a new AI framework for diagnosing Acute Lymphoblastic Leukemia (ALL) with over 96% accuracy. The model enhances efficiency and provides explainable predictions for better clinical use.

Keywords:
ALL detectionDecision support systemEXplainble medical imagingExplainable artificial intelligenceResponsible AIXAI for medical diagnosis

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Area of Science:

  • Medical Diagnostics
  • Artificial Intelligence in Healthcare
  • Hematologic Malignancies

Background:

  • Acute Lymphoblastic Leukemia (ALL) is a severe cancer requiring early diagnosis for effective treatment.
  • Current diagnostic methods face challenges in speed, accuracy, and interpretability.
  • Deep learning models offer potential but often lack transparency.

Purpose of the Study:

  • To develop a novel diagnostic framework for ALL.
  • To improve diagnostic accuracy, computational efficiency, and model explainability.
  • To integrate EfficientNet-B7 with Explainable Artificial Intelligence (XAI) for ALL detection.

Main Methods:

  • Utilized the EfficientNet-B7 deep learning architecture.
  • Integrated Explainable Artificial Intelligence (XAI) techniques: Grad-CAM, CAM, LIME, and Integrated Gradients.
  • Validated the framework on multiple datasets: Taleqani Hospital, C-NMC-19, and Multi-Cancer.

Main Results:

  • Achieved diagnostic accuracies exceeding 96% on the Taleqani dataset and 95.50% on others.
  • Demonstrated superior performance compared to VGG-19, InceptionResNetV2, ResNet50, DenseNet50, and AlexNet.
  • Reduced computational overhead by up to 40% with faster inference times.

Conclusions:

  • The proposed AI framework offers high precision and efficiency for ALL diagnosis.
  • XAI integration provides transparent insights into model predictions.
  • This framework represents a significant advancement for clinical deployment of AI in leukemia diagnosis.