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An Intelligent Model of Segmentation and Classification Using Enhanced Optimization-Based Attentive Mask RCNN and
G Gopichand1, Kovvuri N Bhargavi2, M V S Ramprasad3
1School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
NMR in Biomedicine
|April 24, 2025
Summary
This study introduces a deep learning system using advanced AI to classify multiple sclerosis (MS) from MRI scans. The innovative model achieves high accuracy, improving MS diagnosis and potentially reducing healthcare costs.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Neurology
Background:
- Interpreting magnetic resonance imaging (MRI) for multiple sclerosis (MS) is challenging due to inconsistent clinical-symptom correlation and high costs.
- Accurate MS classification is crucial for effective treatment strategies and patient management.
Purpose of the Study:
- To develop a deep learning system for classifying multiple sclerosis (MS) types using brain MRI scans.
- To enhance diagnostic accuracy and efficiency in MS assessment through advanced AI techniques.
Main Methods:
- A deep learning model integrating attention mechanisms and recurrent networks (RM-LSTM) was developed for MS classification.
- Image segmentation was performed using an adaptive and attentive-based mask regional convolution neural network (AA-MRCNN).
- Model parameters were optimized using an enhanced pine cone optimization algorithm (EPCOA) for improved performance.
Main Results:
- The deep learning model achieved high performance metrics: 95.4% accuracy, 95.3% sensitivity, and 95.4% specificity.
- Experimental analysis demonstrated the model's effectiveness in classifying multiple sclerosis disorder.
Conclusions:
- The developed deep learning system shows significant potential for accurate multiple sclerosis classification from clinical brain MRI.
- This AI-driven approach offers a promising solution to overcome the limitations of traditional MRI interpretation in MS management.
