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Medical image segmentation using an optimized three-tier quantum convolutional neural network trained with hybrid optimization approach

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Abstract

Medical image segmentation is a crucial task in medical image analysis. The proposed method for medical image segmentation involves several steps. First, pre-processing techniques such as Gaussian filtering and contrast stretching are applied to the input image. Next, a region of interest (ROI) is identified from the pre-processed image using an optimized mask RCNN, with the weight function of the RCNN optimized via a new hybrid optimization algorithm- Cuckoo-Spider Optimization, combining Cuckoo Search (CS) and Social Spider Optimization (SSO). After ROI identification, feature extraction is performed, including texture features such as Gray-Level Run Length Matrix (GLRLM), Local rotation invariant Texture Pattern (LrTP), and an Augmented Local Directional Pattern (A-LDP) proposed in this work. Additionally, shape features such as area and perimeter, and color features such as color histogram are extracted. Finally, an optimized three-tier quantum convolutional neural network (O-TT-QCNN) is proposed for segmentation, which can handle complex and heterogeneous medical images. The experimental results demonstrate that the proposed method achieves state-of-the-art performance on several benchmark datasets.

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All the data is collected from the simulation reports of the software and tools used by the authors. Authors are working on implementing the same using real world data with appropriate permissions.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, MLR Institute of Technology, Hyderabad, India

    S. V. S Prasad

  2. Department of Electronics and Communication Engineering, Raghu Engineering College (Autonomous), Visakhapatnam, Andhra Pradesh, India

    B. Chinna Rao

  3. Department of Electronics and Communication Engineering, Sri Vasavi engineering College, Pedatadepalli, Tadepalligudem, Andhra Pradesh, India

    M. Koteswara Rao

  4. Department of Electronics and communication Engineering, N.S.Raju Institute of Technology, Visakhapatnam, Andhra Pradesh, India

    K. Ravi Kumar

  5. Department of Computer Science and Engineering, School of Technology, GITAM Deemed to be University, Hyderabad, India

    Srisailapu D. Vara Prasad

  6. Department of Computer Science and Engineering and Dean IQAC, Aditya Institute of Technology and Management, Tekkali, Andhra Pradesh, India

    Chappa Ramesh

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  1. S. V. S Prasad
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Correspondence to S. V. S Prasad.

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Prasad, S.V.S., Rao, B.C., Rao, M.K. et al. Medical image segmentation using an optimized three-tier quantum convolutional neural network trained with hybrid optimization approach. Multimed Tools Appl 83, 38083–38108 (2024). https://doi.org/10.1007/s11042-023-16980-9

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  • DOI: https://doi.org/10.1007/s11042-023-16980-9

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