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A Framework for Interactive Medical Image Segmentation Using Optimized Swarm Intelligence with Convolutional Neural
Chetna Kaushal1, Md Khairul Islam2, Sara A Althubiti3
1Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India.
Computational Intelligence and Neuroscience
|September 5, 2022
Summary
This study introduces a novel pipeline for medical image segmentation, combining Convolutional Neural Networks (CNNs) with Swarm Intelligence (SI). The K-means with CNN approach achieved the highest segmentation accuracy at 96.45%.
Area of Science:
- Medical Imaging
- Computer Vision
- Artificial Intelligence
Background:
- Advancements in image processing technology significantly impact medical imaging applications.
- Accurate segmentation of diagnostic images is crucial for medical research.
- There is a need for effective deep learning-based segmentation techniques for rapid and precise identification of regions of interest.
Purpose of the Study:
- To propose and evaluate a pipeline for medical image segmentation using Convolutional Neural Networks (CNNs) and Swarm Intelligence (SI).
- To compare the performance of six different segmentation modules, including traditional clustering algorithms and their hybridizations with CNNs and PSO.
Main Methods:
- A pipeline integrating CNNs and SI for image segmentation was developed.
- Six modules were evaluated: Fuzzy C-means (FCM), K-means, FCM with Particle Swarm Optimization (PSO), K-means with PSO, FCM with CNN, and K-means with CNN.
- Experiments were conducted on diverse medical image datasets (MRI, dermoscopic, microscopic, CT) with varying data subset sizes (50 to 2000 images).
Main Results:
- The K-means with CNN module demonstrated superior performance compared to other evaluated methods.
- Achieved a segmentation accuracy of 96.45% with an average processing time of 9.09 seconds.
- Performance was evaluated across multiple medical imaging modalities and dataset sizes.
Conclusions:
- The proposed K-means with CNN approach offers an effective solution for medical image segmentation.
- Hybrid deep learning and swarm intelligence methods show significant promise for improving diagnostic accuracy and efficiency in medical imaging.
- Further research can explore advanced CNN architectures and SI algorithms for enhanced medical image analysis.
