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Home / Equine Research Bank / J Imaging Inform Med / Horse Herd Optimization with Gate Recurrent Unit for an Auto...
Journal of imaging informatics in medicine2024; doi: 10.1007/s10278-023-00962-2

Horse Herd Optimization with Gate Recurrent Unit for an Automatic Classification of Different Facial Skin Disease.

Abstract: The human body's largest organ is the skin which covers the entire body. The facial skin is one area of the body that needs careful handling. It can cause several facial skin diseases like acne, eczema, moles, melanoma, rosacea, and many other fungal infections. Diagnosing these diseases has been difficult due to challenges like the high cost of medical equipment and the lack of medical competence. However, various existing systems are utilized to detect the type of facial skin disease, but those approaches are time-consuming and inaccurate to detect the disease at early stages. To address various issues, a deep learning-based gate recurrent unit (GRU) has been developed. Non-linear diffusion is used to acquire and pre-process raw pictures, adaptive histogram equalization (AHE) and high boost filtering (HBF). The image noise is removed by using non-linear diffusion. The contrast of the image is maximized using AHE. The image's edges are sharpened by using HBF. After pre-processing, textural and colour features are extracted by applying a grey level run-length matrix (GLRM) and chromatic co-occurrence local binary pattern (CCoLBP). Then, appropriate features are selected using horse herd optimization (HOA). Finally, selected features are classified using GRU to identify the types of facial skin disease. The proposed model is investigated using the Kaggle database that consists of different face skin disease images such as rosacea, eczema, basal cell carcinoma, acnitic keratosis, and acne. Further, the acquired dataset is split into training and testing. Considering the investigation's findings, the proposed method yields 98.2% accuracy, 1.8% error, 97.1% precision, and 95.5% f1-score. In comparison to other current techniques, the proposed technique performs better. The created model is, therefore, the best choice for classifying the various facial skin conditions.
© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.
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Publication Date: 2024-01-12 PubMed ID: 38343253PubMed Central: 6256211DOI: 10.1007/s10278-023-00962-2Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

This study focuses on the development of a deep learning-based model to automatically classify different facial skin diseases accurately and efficiently. The model uses a gate recurrent unit (GRU) and combines it with a process called horse herd optimization (HOA) for better results.

Context and Problem Statement

  • The skin, specifically facial skin, can experience several diseases like acne, eczema, moles, melanoma, rosacea, and other fungal infections.
  • Despite the existence of systems that detect skin diseases, they are often time-consuming to use and do not provide accurate early detection of the diseases.

Proposed Solution

  • A gate recurrent unit (GRU), a kind of deep learning algorithm, was used as the foundation of the proposed solution.
  • Images of the skin were first pre-processed using non-linear diffusion to remove any noise, adaptive histogram equalization (AHE) to maximize the contrast of the image, and high boost filtering (HBF) to sharpen the edges of the image.
  • After pre-processing, the model extracts textural and color features from the images using a grey level run-length matrix (GLRM) and chromatic co-occurrence local binary pattern (CCoLBP).
  • Suitable features for classification are then selected using a horse herd optimization (HOA) technique—an optimization process inspired by the social behavior of horse herds.
  • The chosen features are finally classified using the GRU to identify the types of facial skin disease.

Validation and Results

  • The researchers tested the model using the Kaggle database, which comprises images of varied facial skin diseases like rosacea, eczema, basal cell carcinoma, acnitic keratosis, and acne.
  • The results demonstrated a high degree of accuracy, with the proposed method yielding 98.2% accuracy, 1.8% error, 97.1% precision, and a 95.5% f1-score—thus outperforming other existing techniques.
  • Therefore, the researchers conclude that their model is a superior choice for classifying various facial skin conditions.

Cite This Article

APA
Anbalagan E, Malathi S. (2024). Horse Herd Optimization with Gate Recurrent Unit for an Automatic Classification of Different Facial Skin Disease. J Imaging Inform Med. https://doi.org/10.1007/s10278-023-00962-2

Publication

Journal of imaging informatics in medicine
ISSN: 2948-2933
NlmUniqueID: 9918663679206676
Country: Switzerland
Language: English

Researcher Affiliations

Anbalagan, E
  • Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India. eanbalagan77@gmail.com.
Malathi, S
  • Department of Electrical and Electronics Engineering, SRM Valliammai Engineering College, Chengalpattu, Tamil Nadu, 603203, India.

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