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Related Experiment Video

Updated: Sep 13, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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Hyperparameter tuned deep learning-driven medical image analysis for intracranial hemorrhage detection.

Naif Almakayeel1, E Laxmi Lydia2, Oleg Razzhivin3,4,5

  • 1Department of Industrial Engineering, College of Engineering, King Khalid University, Abha, Saudi Arabia.

Plos One
|July 28, 2025
PubMed
Summary

This study introduces a novel AI technique for detecting intracranial hemorrhage (ICH) on CT scans, achieving 99.02% accuracy. The Hyperparameter Tuned Deep Learning-Driven Medical Image Analysis for Intracranial Hemorrhage Detection (HPDL-MIAIHD) method enhances early diagnosis and patient outcomes.

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Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Medicine
  • Neurology

Background:

  • Intracranial hemorrhage (ICH) is a critical medical emergency requiring rapid diagnosis and management.
  • Computerized analysis of CT scans is essential for accurate ICH detection.
  • Deep learning (DL) and artificial intelligence (AI) show promise in medical image analysis.

Purpose of the Study:

  • To propose a novel AI technique, HPDL-MIAIHD, for accurate and efficient detection of ICH from CT scans.
  • To enhance the accuracy and efficiency of ICH detection through advanced DL models and optimization algorithms.

Main Methods:

  • The HPDL-MIAIHD technique utilizes median filtering for preprocessing CT images.
  • An enhanced EfficientNet model, optimized with the Chimp Optimizer Algorithm (COA), extracts features.
  • Ensemble classification using LSTM, SAE, and Bi-LSTM networks, with hyperparameter tuning via Bayesian Optimizer Algorithm (BOA), performs the final detection.

Main Results:

  • The HPDL-MIAIHD approach demonstrated superior performance in detecting ICH on a benchmark CT image dataset.
  • The technique achieved a high accuracy of 99.02%, outperforming existing models.

Conclusions:

  • The proposed HPDL-MIAIHD technique offers a highly accurate and effective solution for automated ICH detection.
  • This AI-driven approach has the potential to significantly improve early diagnosis and patient outcomes for ICH.