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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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A deep convolutional neural network approach using medical image classification.

Mohammad Mousavi1, Soodeh Hosseini2

  • 1Department of Computer Science, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran.

BMC Medical Informatics and Decision Making
|August 29, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an automatic COVID-19 detection model using cough sounds and medical images. The model achieved high accuracy, aiding in rapid screening and diagnosis of Coronavirus disease.

Keywords:
Convolutional neural networksDeep learningInternet of health thingsMedical image classification

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Epidemiology

Background:

  • The rapid global spread of epidemic diseases like COVID-19 necessitates early and accurate diagnosis.
  • Timely medical intervention and public health measures depend on effective disease detection.

Purpose of the Study:

  • To propose an automated COVID-19 detection model integrating respiratory sound analysis and medical image interpretation.
  • To enhance the speed and accuracy of COVID-19 diagnosis using Internet of Health Things (IoHT) technologies.

Main Methods:

  • Cough sounds were analyzed to differentiate between healthy individuals and those with COVID-19, achieving 94.999% accuracy.
  • Pre-trained convolutional neural network models (InceptionResNetV2, InceptionV3, EfficientNetB4) were employed for classifying chest X-rays and CT scans.
  • Transfer learning was utilized on two datasets of medical images for improved disease identification.

Main Results:

  • The cough sound analysis model demonstrated high efficacy in initial COVID-19 screening.
  • InceptionResNetV2 achieved 99.414% accuracy for CT-scan image classification.
  • InceptionV3 and EfficientNetB4 models attained 96.943% accuracy for radiology image classification.

Conclusions:

  • The proposed IoHT-based model offers a robust solution for rapid and accurate COVID-19 detection.
  • This automated system supports radiologists in confirming diagnoses and facilitates public health screening.