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Deep Neural Networks for Image-Based Dietary Assessment
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Learning deep neural networks' architectures using differential evolution. Case study: Medical imaging processing.

Smaranda Belciug1

  • 1Department of Computer Science, Faculty of Sciences, University of Craiova, Craiova, 200585, Romania.

Computers in Biology and Medicine
|June 25, 2022
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) can accelerate medical diagnoses. This study introduces a novel method using differential evolution to automatically design deep learning networks, improving accuracy in cancer and maternal-fetal imaging.

Keywords:
Cancer MRI scanCancer histopathological imageDeep learningDifferential evolutionMaternal-fetal ultrasoundStatistical assessment

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Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Healthcare
  • Computational Biology

Background:

  • The COVID-19 pandemic disrupted medical practices, particularly in cancer and obstetric care, leading to increased preventable deaths and complications.
  • Delays in cancer diagnosis and maternal-fetal monitoring necessitate faster, more accurate diagnostic tools.
  • Deep learning excels at image classification but requires manual network architecture design and performance verification.

Purpose of the Study:

  • To propose an automated method for learning deep neural network architectures.
  • To address the exponential increase in network architectures by using a differential evolution algorithm.
  • To enhance diagnostic speed and accuracy in critical medical fields.

Main Methods:

  • Developed a method to encode network structures as fixed-length integer arrays.
  • Implemented a differential evolution algorithm including mutation, recombination, and selection processes.
  • Tested the model on diverse datasets: three cancer (MRI, histopathology) and two maternal-fetal ultrasound images.

Main Results:

  • The proposed automated deep learning architecture search method achieved competitive performance.
  • Accuracy ranged from 78.73% to 99.50% across various medical imaging datasets.
  • Benchmarked against VGG16, ResNet50, Inception V3, and DenseNet169, demonstrating strong results.

Conclusions:

  • Automated deep learning architecture search using differential evolution offers a promising solution for medical image analysis.
  • The method shows potential to improve diagnostic efficiency and accuracy in cancer and obstetric care.
  • This approach can help mitigate the impact of delayed diagnoses in critical healthcare scenarios.