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Related Experiment Video

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Optimal Deep Transfer Learning-Based Human-Centric Biomedical Diagnosis for Acute Lymphoblastic Leukemia Detection.

Manar Ahmed Hamza1, Amani Abdulrahman Albraikan2, Jaber S Alzahrani3

  • 1Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, AlKharj, Saudi Arabia.

Computational Intelligence and Neuroscience
|June 9, 2022
PubMed
Summary

This study introduces an optimal deep transfer learning model for detecting acute lymphoblastic leukemia in blood smears. The model enhances diagnostic accuracy for this serious cancer using advanced image analysis techniques.

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Area of Science:

  • Biomedical Engineering
  • Medical Imaging Analysis
  • Artificial Intelligence in Healthcare

Background:

  • Human-centric biomedical diagnosis (HCBD) is crucial for clinical decision-making, particularly for diseases like leukemia.
  • Deep learning (DL) models show promise in medical image analysis, but require large datasets for training.
  • Transfer learning offers a solution for feature extraction in DL models when data is limited.

Purpose of the Study:

  • To develop an optimal deep transfer learning-based model for acute lymphoblastic leukemia detection (ODLHBD-ALLD).
  • To accurately detect and classify acute lymphoblastic leukemia using blood smear images.
  • To improve the efficiency and accuracy of computer-aided diagnosis in healthcare.

Main Methods:

  • Utilized Gabor filtering (GF) for noise reduction in blood smear images.
  • Employed a modified fuzzy c-means (MFCM) algorithm for image segmentation.
  • Integrated Competitive Swarm Optimization (CSO) with EfficientNetB0 for feature extraction.
  • Applied an attention-based long-short term memory (ABiLSTM) model for classification.

Main Results:

  • The ODLHBD-ALLD model demonstrated superior performance in detecting and classifying acute lymphoblastic leukemia.
  • Simulations on an open-access dataset confirmed the model's effectiveness.
  • Comparative analysis showed significant improvements over existing diagnostic approaches.

Conclusions:

  • The proposed ODLHBD-ALLD model offers an effective deep transfer learning solution for acute lymphoblastic leukemia detection.
  • The integration of GF, MFCM, CSO-EfficientNetB0, and ABiLSTM enhances diagnostic accuracy.
  • This approach contributes to advancing computer-aided detection in biomedical diagnosis.