Adversarial attacks on hybrid classical-quantum Deep Learning models for Histopathological Cancer Detection

• URL: http://arxiv.org/abs/2309.06377v1
• Date: Fri, 8 Sep 2023 06:37:54 GMT
• Title: Adversarial attacks on hybrid classical-quantum Deep Learning models for Histopathological Cancer Detection
• Authors: Biswaraj Baral, Reek Majumdar, Bhavika Bhalgamiya, and Taposh Dutta Roy
• Abstract summary: The study emphasizes two primary applications of hybrid classical-quantum Deep Learning models. We compare the performance accuracy of the classical model with the hybrid classical-quantum model using pennylane default quantum simulator.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present an effective application of quantum machine learning in histopathological cancer detection. The study here emphasizes two primary applications of hybrid classical-quantum Deep Learning models. The first application is to build a classification model for histopathological cancer detection using the quantum transfer learning strategy. The second application is to test the performance of this model for various adversarial attacks. Rather than using a single transfer learning model, the hybrid classical-quantum models are tested using multiple transfer learning models, especially ResNet18, VGG-16, Inception-v3, and AlexNet as feature extractors and integrate it with several quantum circuit-based variational quantum circuits (VQC) with high expressibility. As a result, we provide a comparative analysis of classical models and hybrid classical-quantum transfer learning models for histopathological cancer detection under several adversarial attacks. We compared the performance accuracy of the classical model with the hybrid classical-quantum model using pennylane default quantum simulator. We also observed that for histopathological cancer detection under several adversarial attacks, Hybrid Classical-Quantum (HCQ) models provided better accuracy than classical image classification models.

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