Horse Herd Optimization with Gate Recurrent Unit for an Automatic Classification of Different Facial Skin Disease.
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 di...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 2; pp. 814 - 831 |
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| Autores principales: | , |
| Formato: | algorithm equations & formulas pictorial tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=177626012&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 177626012 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Apr2024 vid: 37 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 177626012 177626012 177626012 10.1007/s10278-023-00962-2 177626012 ppf: 814 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Horse Herd Optimization with Gate Recurrent Unit for an Automatic Classification of Different Facial Skin Disease. aug: au: Anbalagan, E. Malathi, S. affil: Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India sug: subj: Skin Diseases Face Pathology Time Factors Early Diagnosis Neural Networks (Computer) Deep Learning Acne Rosacea Eczema Carcinoma, Basal Cell Keratosis, Actinic Acne Vulgaris ab: 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. pubtype: Academic Journal doctype: algorithm equations & formulas pictorial tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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