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

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 i...

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Bibliographic Details
Published in:Multimedia tools and applications 2024-04, Vol.83 (13), p.38083-38108
Main Authors: Prasad, S. V. S, Rao, B. Chinna, Rao, M. Koteswara, Kumar, K. Ravi, Vara Prasad, Srisailapu D., Ramesh, Chappa
Format: Article
Language:English
Subjects:
Artificial neural networks
Color
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Feature extraction
Image analysis
Image contrast
Image segmentation
Medical imaging
Multimedia Information Systems
Neural networks
Optimization
Search algorithms
Special Purpose and Application-Based Systems
Texture
Weighting functions
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Summary: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.
ISSN:1573-7721
1380-7501
1573-7721
DOI:10.1007/s11042-023-16980-9