Speckle classification of a multimode fiber based onInception V3
Zifei Li, Leihong Zhang, Zili Zhang, Runchu Xu, Dawei Zhang
Abstract
Multimode optical fiber plays an important role in endoscope miniaturization. With the development of deep learning and machine learning, neural networks can be used to identify and classify speckle patterns obtained at the fiber output. Based on the speckle pattern of a HERLEV dataset cell image transmitted by a multimode fiber, this paper studies the recognition accuracy of various types of speckle by a support vector machine, K-nearest neighbor, and convolutional neural network (Inception V3). Meanwhile, we propose an image classification optimization algorithm based on improved Inception V3. The experimental results show that the improved algorithm model is better than the traditional machine learning method; the accuracy rate is 97.92%, which effectively improves the performance of the pathological cell diagnosis deep learning model and lays a theoretical and practical foundation for further clinical application.