SSDA_AOA: Stacked Sparse Denoising Autoencoder With Archimedes Optimization Algorithm Based Oral Cancer Detection on Histopathological Images.
Objectives: This study aims to address the challenges in the diagnosis of oral cancer by proposing a novel computer‐aided diagnostic framework that leverages advanced deep learning (DL) and optimization techniques to enhance early detection and improve patient outcomes. Materials and Methods: In the...
| Publicado en: | Oral Diseases Vol. 32; no. 6; pp. 1556 - 1571 |
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| Autores principales: | , |
| Formato: | algorithm equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Jun2026
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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=196087810&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 196087810 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1354523X DZP jtl: Oral Diseases issn: 1354523X maglogo: Y pubinfo: dt: Jun2026 vid: 32 iid: 6 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 196087810 191253783 196087810 196087810 10.1111/odi.70210 196087810 ppf: 1556 ppct: 15 formats: tig: atl: SSDA_AOA: Stacked Sparse Denoising Autoencoder With Archimedes Optimization Algorithm Based Oral Cancer Detection on Histopathological Images. aug: au: Sathish Kumar, R. Govindarajan, M. affil: Department of Computer Science and Engineering, Annamalai University, Chidambaram Tamil Nadu, , India sug: subj: Conceptual Framework Algorithms Autoencoder Mouth Neoplasms Diagnosis Mouth Neoplasms Pathology Diagnosis, Computer Assisted Early Detection of Cancer Deep Learning Early Intervention Image Processing, Computer Assisted Treatment Outcomes Human Cancer Patients Descriptive Statistics Sensitivity and Specificity Artificial Intelligence Neural Networks (Computer) Cancer Screening Machine Learning Algorithms Mouth Neoplasms Classification Image Enhancement ab: Objectives: This study aims to address the challenges in the diagnosis of oral cancer by proposing a novel computer‐aided diagnostic framework that leverages advanced deep learning (DL) and optimization techniques to enhance early detection and improve patient outcomes. Materials and Methods: In the framework proposed, the histopathological images are subjected to a preprocessing technique, and then, the images are fed directly to the NASNet‐Large model for the extraction of high‐level discriminative texture features. The resultant vectors obtained from the features extracted act as input to the search space of Archimedes Optimization Algorithm that carries out dimensionality reduction and optimal hyperparameter tuning simultaneously. The optimized feature subset is fed to the final classifier, namely the Stacked Sparse Denoising Autoencoder that learns robust latent representations. Results: The findings demonstrate that the proposed approach achieves superior performance, achieving an accuracy of 95.38%, a precision of 95.15%, a sensitivity of 91.78%, a specificity of 91.85%, and an F1‐score of 93.72%. Conclusions: These findings underscore the potential of the SSDA‐AOA framework as an effective tool for the early detection and precise classification of oral cancer, paving the way for improved patient outcomes through timely intervention. Clinical Relevance: This innovative approach may significantly enhance patient outcomes by facilitating earlier diagnosis and treatment, addressing the urgent need for more reliable diagnostic tools in oncology. pubtype: Academic Journal doctype: algorithm equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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