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Improved Golden Eagle Optimization Based CNN for Automatic Segmentation of Psoriasis Skin Images

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Abstract

Psoriasis is a condition of the skin, it causes skin cells to swell, resulting in itchy red patches. Psoriasis now impacts 40% of the population. In current days, technology has emerged as the primary method for identifying psoriatic skin condition by segmenting affected skin images. Furthermore, several researchers have reported promising machine learning approaches for segmenting psoriasis skin images. Even now, the model’s accuracy and time consumption need to be enhanced more consequently, in this paper, we offer an Adaptive Golden Eagle Optimization (IGEO) on the basis of Convolutional Neural Network (CNN) for autonomous segmentation of psoriasis skin images. Following pre-processing, the input images are segmented by the IGEO-CNN approach, with the weight and bias parameters of CNN tuned using this IGEO. Modifying the random sequence on the basis of tent map increases the study effectiveness of the GEO approach. Finally, artifacts are eliminated from the segmented output images using the threshold module. We achieve 97% accuracy via simulation.

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The authors declare that they have competing interests and funding.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Lovely Professional University, Punjab, India

    Karthick Panneerselvam

  2. Department of Computer Science and Engineering, Rajeev Gandhi Memorial College of Engineering and Tecnology, Nandyal, AP, India

    P. Prathap Nayudu

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  1. Karthick Panneerselvam
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  2. P. Prathap Nayudu
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Correspondence to Karthick Panneerselvam.

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Panneerselvam, K., Nayudu, P.P. Improved Golden Eagle Optimization Based CNN for Automatic Segmentation of Psoriasis Skin Images. Wireless Pers Commun 131, 1817–1831 (2023). https://doi.org/10.1007/s11277-023-10522-0

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