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Hybrid Optimization Enabled Segmentation with Deep Learning For Histopathological Images of Uterine Tissue

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Abstract

The technique of modifying an image to make it better suited for a certain use than the original image is known as image enhancement. Digitalized picture enhancement systems provide numerous choices for improving image visual quality. Imaging modalities, observation settings, and other factors all have a substantial impact on the appropriate choosing of various methodologies. The Fractional Pelican Crow Search Algorithm improved SQA (FPCSO_ enhanced SQA) is a novel image enhancement technique for histopathology imaging of uterine tissue that is provided here. The database is initially generated, and the image is forwarded to the pre-processing phase. In order to reduce noise and enhance image quality, the median filter is employed in image pre-processing. Additionally, image enhancement is done utilizing the Pelican Crow Search Algorithm-trained Multiple Identities Representation Network. After the image enhancement, the tissue segmentation is performed using proposed segmentation quality assessment network, where parameter selection is based on Deep Convolutional Neural Network (DCNN). Here, DCNN is trained using proposed FPCSO algorithm. Accordingly, proposed FPCSO approach is newly integrated by the combination of Pelican Optimization Algorithm, Crow Search Optimization and Fractional calculus. Furthermore, FPCSO-enhanced SQAreached best consequences with maximal Peak signal-to-noise ratio (PSNR) of 49.574 dB, minimum Mean Square Error of 0.975, and minimum degree of distortion of 0.061 dB, correspondingly.

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Data availability

The data underlying this article are available in Cancer imaging archive dataset, at https://www.cancerimagingarchive.net/datascope/cptac/home/.

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Acknowledgements

I would like to express my very great appreciation to the co-authors of this manuscript for their valuable and constructive suggestions during the planning and development of this research work.

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This research did not receive any specific funding.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Basaveshwar Engineering College, Bagalkote, Karnataka, 587102, India

    Veena I. Patil

  2. BLDEA’s V. P Dr P. G Halakatti College of Engineering and Technology, Vijayapura, Karnataka, 586103, India

    Veena I. Patil

  3. Information Science and Engineering, Department of Information Science and Engineering, Basaveshwar Engineering College/ Visvesveraya Technological University, Bagalkote, Karnataka, 587102, India

    Shobha R. Patil

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  1. Veena I. Patil
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All authors have made substantial contributions to conception and design, revising the manuscript, and the final approval of the version to be published. Also, all authors agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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Correspondence to Veena I. Patil.

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Patil, V.I., Patil, S.R. Hybrid Optimization Enabled Segmentation with Deep Learning For Histopathological Images of Uterine Tissue. Sens Imaging 25, 13 (2024). https://doi.org/10.1007/s11220-023-00456-z

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