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Speckle classification of a multimode fiber based on Inception V3
Applied Optics
|October 18, 2022
Summary
This study uses deep learning to analyze cell images transmitted through multimode optical fibers. An improved Inception V3 model achieved 97.92% accuracy in classifying speckle patterns for pathological cell diagnosis.
Area of Science:
- Biomedical Optics
- Medical Imaging
- Artificial Intelligence in Medicine
Background:
- Multimode optical fibers are crucial for miniaturizing endoscopes.
- Deep learning and machine learning enable neural networks to analyze speckle patterns from fiber optics.
Purpose of the Study:
- To evaluate speckle pattern recognition accuracy for pathological cell diagnosis using various machine learning models.
- To propose and validate an optimized deep learning algorithm for improved cell image classification.
Main Methods:
- Utilized a HERLEV dataset of cell images transmitted via multimode optical fiber.
- Compared Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Convolutional Neural Network (CNN) including Inception V3.
- Developed an image classification optimization algorithm based on an improved Inception V3 model.
Main Results:
- The improved Inception V3 algorithm demonstrated superior performance compared to traditional machine learning methods.
- Achieved a high accuracy rate of 97.92% in classifying speckle patterns.
- The model effectively enhanced the performance of deep learning models for pathological cell diagnosis.
Conclusions:
- The proposed optimized deep learning model significantly improves pathological cell diagnosis accuracy.
- This research provides a strong theoretical and practical foundation for the clinical application of fiber-optic-based imaging.
