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Updated: Jun 28, 2025
Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
Published on: October 27, 2023
A Novel Detection of Cerebrovascular Disease using Multimodal Medical Image Fusion
1Department of Biomedical Engineering, North-Eastern Hill University, Shillong, India.
Insights
This study introduces a robust framework for detecting cerebrovascular diseases using advanced machine learning models. A cascaded model achieved 96.21% accuracy, outperforming other methods for improved disease diagnosis.
Area of Science:
- Medical Imaging Analysis
- Machine Learning in Healthcare
- Neurology
Background:
- Cerebrovascular diseases stem from various causes like thrombosis and atherosclerosis.
- Accurate detection is crucial for timely intervention and patient outcomes.
Purpose of the Study:
- To propose a robust framework for cerebrovascular disease detection.
- To validate the proposed framework on diverse datasets.
Main Methods:
- Model 1: Fused CT and MR images using Discrete Fourier Transform, classified with machine learning and pre-trained models.
- Model 2: Proposed a cascaded deep learning model for enhanced detection.
Main Results:
- Support Vector Machine achieved 92% accuracy with specific feature engineering.
- Inception V3 model reached 95.6% accuracy.
- The cascaded model demonstrated superior performance with 96.21% accuracy.
Conclusions:
- The cascaded model shows significant potential for accurate cerebrovascular disease detection.
- Validation on external datasets confirmed accuracy improvements, highlighting clinical applicability.
Background:
Diseases are medical situations that are allied with specific signs and symptoms. A disease may be instigated by internal dysfunction or external factors like pathogens. Cerebrovascular disease can progress from diverse causes, comprising thrombosis, atherosclerosis, cerebral venous thrombosis, or embolic arterial blood clot.
Objective:
In this paper, authors have proposed a robust framework for the detection of cerebrovascular diseases employing two different proposals which were validated by use of other datasets.
Methods:
In proposed model 1, the Discrete Fourier transform is used for the fusion of CT and MR images which was classified them using machine learning techniques and pre-trained models while in proposed model 2, the cascaded model was proposed. The performance evaluation parameters like accuracy and losses were evaluated.
Results:
92% accuracy was obtained using Support Vector Machine using Gray Level Difference Statistics and Shape features with Principal Component Analysis as a feature selection technique while Inception V3 resulted in 95.6% accuracy while the cascaded model resulted in 96.21% accuracy.
Conclusion:
The cascaded model is later validated on other datasets which results in 0.11% and 0.14% accuracy improvement over TCIA and BRaTS datasets respectively.
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