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IEEE Access

Underwater Animal Identification and Classification Using a Hybrid Classical-Quantum Algorithm

Sheena Christabel Pravin, G. Rohith, V. Kiruthika, E. Manikandan, S. Methelesh, Akash L. Manoj

Published Jan 1, 2023• 14 citations
Publisher

Abstract

Underwater Animal Identification and Classification is gaining significance in recent times as a result of the increased need for biodiversity monitoring and ecological surveillance. Although classical deep learning techniques have been widely used for these tasks, the effectiveness of identification and classification remains a bottleneck for several researchers due to the live capture of animals in complex environments, a limited sea-animal image dataset, and the complex topography of the seafloor, particularly in shallow waters, sediments, reefs, submarine ridges, and ship radiation. In this paper, three hybrid Classical-Quantum neural networks ResNet50-QCNN, ResNet18-QCNN and InceptionV3-QCNN have been proposed for underwater quantum-classical Animal Identification and Classification. By using quantum devices to reduce dimension and denoise datasets, it dramatically reduces the complexity of data processing on classical computers. The outcomes of the numerical simulation show that the quantum algorithm may effectively reduce dimensionality and increase classification accuracy. Even when quantum data is read out classically, the hybrid approach provides polynomial acceleration in dimension reduction beyond classical procedures. The three hybrid models, viz., ResNet50-QCNN, ResNet18-QCNN, and InceptionV3-QCNN, displayed classification test accuracy of 88%, 80.29%, and 70%, respectively, revealing that ResNet50-QCNN performed best in identifying and classifying underwater animals.

DOI
10.1109/access.2023.3343120

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With millions of medical data being produced daily, it is important to leverage current technology to improve the healthcare system. As traditional deep learning models are unable to effectively handle high-dimensional data, a hybrid classical-quantum approach is used to enhance traditional deep learning algorithms. The hybrid classical-quantum models are built upon the classical deep learning framework with quantum techniques. The classical layers are utilized for feature extraction while the quantum layer is used for computational tasks. This addresses computational limitations and data imbalance challenges in medical image analysis. Transfer learning models, such as ResNet18 and InceptionV3, are used in conjunction with a quantum variational circuit to perform the task. The model is trained on a chest X-ray dataset, which consists of two sets of data: binary and multiclass. The binary dataset consists of Normal and COVID-19 images, while the multiclass dataset has COVID-19, Normal, and Tuberculosis images for classification. PennyLane, as a simulator, has been used to perform quantum computing. The dataset has also been used to train the traditional transfer learning models, pre-trained ResNet18, and their results have been compared. Out of all the models tested, the hybrid classical-quantum model, namely Hybrid ResNet18, performed the best. It achieved an accuracy of 93.22% for the binary classification task and 93.91% for the multiclass classification. The results show that there is a statistically significant improvement in classification accuracy and F1-score of the hybrid approach over the classical transfer learning baselines under a simulation-based environment. This sets the stage for further research on real-world physical quantum hardware. Class imbalance and overlapping predictions have been addressed using methods such as data augmentation and confusion matrix analysis. Thus, quantum machine learning could be used as a vital tool to analyze medical data for better diagnostic results.
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