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DESIGN AND ANALYSIS OF BACTERIAL IMAGE CLASSIFICATION FOR CLINICAL APPLICATIONS: A SYSTEMATIC REVIEW

  • Unique Paper ID: 179625
  • Volume: 11
  • Issue: 12
  • PageNo: 7884-7889
  • Abstract:
  • Precise bacterial identification is essential in medical diagnostics, affecting treatment approaches, antibiotic resistance surveillance, and infection management. Conventional culture-based techniques are still prevalent but are hindered by protracted processes and subjective bacterial classification. This comprehensive review analyzes progress in bacterial image classification, emphasizing deep learning methodologies, image processing methods, and computer models utilized in clinical microbiology. The review examines feature extraction, CNN-based classification models, and AI-driven bacterial recognition systems, evaluating their efficacy in accurately identifying harmful bacteria. Critical problems, such discrepancies in image quality, constraints of datasets, and the generalizability of models, are examined to underscore deficiencies in current techniques. The findings from survey demonstrate that machine learning and deep learning frameworks markedly enhance classification accuracy, facilitating swift, automated bacterial diagnosis for immediate therapeutic use. The future necessitates high-quality labeled bacterial imaging datasets, enhanced AI model interpretability, and integration with electronic health records (EHRs) for efficient medical diagnosis. This paper establishes a basis for creating scalable, AI-driven bacterial categorization systems, promoting progress in predictive microbiology and tailored medicine.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 12
  • PageNo: 7884-7889

DESIGN AND ANALYSIS OF BACTERIAL IMAGE CLASSIFICATION FOR CLINICAL APPLICATIONS: A SYSTEMATIC REVIEW

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8.01 (Year 2024)
UGC Approved
Journal no 47859

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