Efficient anomaly detection from medical signals and images with convolutional neural networks for Internet of medical things (IoMT) systems.

Journal: International journal for numerical methods in biomedical engineering
Published Date: Dec 18, 2021

Abstract

Deep learning is one of the most promising machine learning techniques that revolutionalized the artificial intelligence field. The known traditional and convolutional neural networks (CNNs) have been utilized in medical pattern recognition applications that depend on deep learning concepts. This is attributed to the importance of anomaly detection (AD) in automatic diagnosis systems. In this paper, the AD is performed on medical electroencephalography (EEG) signal spectrograms and medical corneal images for Internet of medical things (IoMT) systems. Deep learning based on the CNN models is employed for this task with training and testing phases. Each input image passes through a series of convolution layers with different kernel filters. For the classification task, pooling and fully-connected layers are utilized. Computer simulation experiments reveal the success and superiority of the proposed models for automated medical diagnosis in IoMT systems.

Authors

  • Ali A Khalil
    Department of Electronics and Communications, Faculty of Engineering, Minia University, Minia, Egypt.
  • Fatma E Ibrahim
    Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt.
  • Mohamed Y Abbass
    Engineering Department, Nuclear Research Center, Atomic Energy Authority, Cairo, Egypt.
  • Nehad Haggag
    Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt.
  • Yasser Mahrous
    Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt.
  • Ahmed Sedik
    Department of the Robotics and Intelligent Machines, Kafrelsheikh University, Kafrelsheikh 33511, Egypt.
  • Zeinab Elsherbeeny
    Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt.
  • Ashraf A M Khalaf
    Electronics and Communications Department, Faculty of Engineering, Minia University, Minia, Egypt. Electronic address: ashkhalaf@yahoo.com.
  • Mohamad Rihan
    Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt.
  • Walid El-Shafai
    Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt.
  • Ghada M El-Banby
    Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt.
  • Eman Soltan
    Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt.
  • Naglaa F Soliman
    Electronics and Communication Department Faculty of Engineering, Zagazig University, Zagazig, Egypt.
  • Abeer D Algarni
    Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 84428, Riyadh 11671, Saudi Arabia.
  • Waleed Al-Hanafy
    Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt.
  • Adel S El-Fishawy
    Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt.
  • El-Sayed M El-Rabaie
    Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt.
  • Waleed Al-Nuaimy
    Department of Electrical Engineering and Electronics, The University of Liverpool, Liverpool, UK.
  • Moawad I Dessouky
    Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt.
  • Adel A Saleeb
  • Nagy W Messiha
    Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt.
  • Ibrahim M El-Dokany
    Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt.
  • Mohsen A M El-Bendary
    Department of Electronics Technology, Faculty of Technology and Education, Helwan University, Cairo, Egypt.
  • Fathi E Abd El-Samie
    Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufa University, Menouf 32952, Egypt.

Keywords

External Resources

Popular Topics
Recent Journals