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MRI based early Temporal Lobe Epilepsy detection using DGWO based optimized HAETN and Fuzzy-AAL Segmentation

Hasim Khan1, Ahmed Ibrahim Alutaibi2, Ghanshyam G Tejani3,4

  • 1Department of Mathematics, College of Science, Jazan University, Jazan, Kingdom of Saudi Arabia.

Plos One
|July 2, 2025
PubMed
Summary

This study introduces a novel deep learning model for early Temporal Lobe Epilepsy (TLE) diagnosis. The Hybrid Attention-Enhanced Transformer Network (HAETN) achieves high accuracy, improving TLE detection and patient outcomes.

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Area of Science:

  • Neurology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Current Temporal Lobe Epilepsy (TLE) detection methods face limitations in MRI sequence applicability, seizure onset localization, and generalizability across patient groups.
  • These limitations hinder the practical clinical application of existing TLE diagnostic tools.

Purpose of the Study:

  • To develop an advanced deep learning model for early and accurate diagnosis of Temporal Lobe Epilepsy (TLE).
  • To overcome the limitations of current TLE detection techniques and improve patient outcomes.

Main Methods:

  • Introduction of a Hybrid Attention-Enhanced Transformer Network (HAETN) for TLE diagnosis.
  • Utilizing the Fuzzy-AAL Segmentation Framework (FASF), combining Fuzzy Possibilistic C-Means (FPCM) for tissue segmentation and AAL for tissue labeling.
  • Implementation of Dipper-grey wolf optimization (DGWO) for effective feature selection to enhance model performance.

Main Results:

  • The proposed HAETN model achieved high performance metrics on the Temporal Lobe Epilepsy-UNAM MRI Dataset.
  • Achieved an accuracy of 98.61%, sensitivity of 99.83%, and F1-score of 99.82%.

Conclusions:

  • The developed HAETN model demonstrates significant efficiency and clinical applicability for early TLE diagnosis.
  • The advanced deep learning approach shows promise in minimizing the impact of epilepsy on individuals and society.