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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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ALL classification using neural ensemble and memetic deep feature optimization.

Muhammad Awais1,2, Riaz Ahmad3,4, Nabeela Kausar3

  • 1Department of Electrical and Computer Engineering, COMSATS University Islamabad, Wah, Pakistan.

Frontiers in Artificial Intelligence
|April 24, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an advanced deep learning method for accurately detecting and classifying subtypes of acute lymphoblastic leukemia (ALL) from blood smear images, achieving high diagnostic accuracy.

Keywords:
convolutional neural networkdeep neural networksmeta-heuristicsoptimizationtransfer learning

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Area of Science:

  • Hematology
  • Computational Pathology
  • Artificial Intelligence in Medicine

Background:

  • Acute lymphoblastic leukemia (ALL) is a critical blood disorder requiring precise detection for effective treatment.
  • Deep convolutional neural networks (CNNs) show potential in digital pathology but struggle with subtle leukemia subtype differences.
  • Accurate classification of ALL subtypes is crucial for prognosis and personalized treatment strategies.

Purpose of the Study:

  • To develop an improved pipeline for the binary detection and subtype classification of ALL using blood smear images.
  • To enhance the accuracy and efficiency of ALL diagnosis through advanced deep learning and optimization techniques.
  • To address the challenges posed by subtle morphological variations among leukemia subtypes.

Main Methods:

  • A customized 88-layer deep CNN was developed and trained using transfer learning with GoogleNet CNN for feature ensemble creation.
  • Feature selection was modeled as a combinatorial optimization problem, employing a memetic binary whale optimization algorithm with Differential Evolution.
  • The proposed approach was validated on standard public datasets of peripheral blood smear images.

Main Results:

  • Achieved an overall best average accuracy of 99.15% for binary ALL classification, with a significant 85% reduction in feature vector size.
  • Demonstrated high performance metrics for binary classification: 99% precision and 98.8% sensitivity.
  • Attained 98.69% accuracy for B-ALL subtype classification, with 98.7% precision and 99.57% specificity.

Conclusions:

  • The proposed deep learning pipeline significantly improves the accuracy and efficiency of acute lymphoblastic leukemia detection and subtype classification.
  • The integration of CNNs with advanced optimization algorithms offers a powerful tool for digital pathology in hematological diagnostics.
  • This methodology outperforms existing studies, highlighting its potential for clinical application in diagnosing ALL.