利用迁移学习进行量子启发的阿雷卡努X射线图像分类

Praveen M Naik, Bhawana Rudra
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引用次数: 0

摘要

火麻仁的 X 射线图像能准确反映其内部结构。研究采用传统的卷积神经网络(CNN)和先进的量子卷积神经网络(QCNN)方法,对基于迁移学习的分类进行了比较分析。研究探索了各种不同规模的迁移学习模型,以确定最适合的模型,从而提高准确率。使用 QCNN 方法,规模因子为 2.0 的 Shufflenet 模型分类准确率最高,达到 97.72%,模型大小为 28.40 MB。在测试的 12 个迁移学习模型中,与传统的基于 CNN 的迁移学习方法相比,使用 QCNN 模型时,9 个模型的分类准确率有所提高。因此,对基于 CNN 和 QCNN 的分类方法的探索表明,在迁移学习框架内,QCNN 的准确性优于传统的 CNN 模型。对量子比特的进一步实验表明,在这种情况下,利用 4 个量子比特进行分类操作是最佳选择。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quantum‐inspired Arecanut X‐ray image classification using transfer learning
Arecanut X‐ray images accurately represent their internal structure. A comparative analysis of transfer learning‐based classification, employing both a traditional convolutional neural network (CNN) and an advanced quantum convolutional neural network (QCNN) approach is conducted. The investigation explores various transfer learning models with different sizes to identify the most suitable one for achieving enhanced accuracy. The Shufflenet model with a scale factor of 2.0 attains the highest classification accuracy of 97.72% using the QCNN approach, with a model size of 28.40 MB. Out of the 12 transfer learning models tested, 9 exhibit improved classification accuracy when using QCNN models compared to the traditional CNN‐based transfer learning approach. Consequently, the exploration of CNN and QCNN‐based classification reveals that QCNN outperforms traditional CNN models in accuracy within the transfer learning framework. Further experiments with qubits suggest that utilising 4 qubits is optimal for classification operations in this context.
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