Mujeeb Ur Rehman, Arslan Shafique, Imdad Ullah Khan, Yazeed Yasin Ghadi, Jawad Ahmad, Mohammed S. Alshehri, Mimonah Al Qathrady, Majed Alhaisoni, Muhammad H. Zayyan

An efficient deep learning model for brain tumour detection with privacy preservation

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction
  • Information Systems
Save to CiteDrive Show PDF

AbstractInternet of medical things (IoMT) is becoming more prevalent in healthcare applications as a result of current AI advancements, helping to improve our quality of life and ensure a sustainable health system. IoMT systems with cutting‐edge scientific capabilities are capable of detecting, transmitting, learning and reasoning. As a result, these systems proved tremendously useful in a range of healthcare applications, including brain tumour detection. A deep learning‐based approach for identifying MRI images of brain tumour patients and normal patients is suggested. The morphological‐based segmentation method is applied in this approach to separate tumour areas in MRI images. Convolutional neural networks, such as LeNET, MobileNetV2, Densenet and ResNet, are tested to be the most efficient ones in terms of detection performance. The suggested approach is applied to a dataset gathered from several hospitals. The effectiveness of the proposed approach is assessed using a variety of metrics, including accuracy, specificity, sensitivity, recall and F‐score. According to the performance evaluation, the accuracy of LeNET, MobileNetV2, Densenet, ResNet and EfficientNet is 98.7%, 93.6%, 92.8%, 91.6% and 91.9%, respectively. When compared to the existing approaches, LeNET has the best performance, with an average of 98.7% accuracy.

Need a simple solution for managing your BibTeX entries? Explore CiteDrive!

  • Web-based, modern reference management
  • Collaborate and share with fellow researchers
  • Integration with Overleaf
  • Comprehensive BibTeX/BibLaTeX support
  • Save articles and websites directly from your browser
  • Search for new articles from a database of tens of millions of references
Try out CiteDrive

More from our Archive