Keywords:
pathogen classification
diagnostics
sunflower optimized naive bayes classifier (SONBC)
gaussian filter
independent component analysis (ICA)

  • Abstract

    Microorganism image analysis is a critical tool in infectious disease diagnostics, permitting accurate identification and classification of pathogens in clinical samples. By incorporating Artificial Intelligence (AI), this process becomes crucially more efficient and precise, with AI algorithms trained to classify features such as morphology, size, and color. Despite, challenges like background noise, staining inconsistencies, and image distortions can hinder analysis, entailing advanced preprocessing and feature extraction techniques. This research presents the Sunflower Optimized Naive Bayes Classifier (SONBC), a novel approach aimed at addressing these challenges and improving diagnostic performance. The process begins with the collection of a comprehensive dataset of microorganism images, followed by preprocessing using the Gaussian Filter to reduce noise and enhance image quality. Feature extraction is conducted utilizing Independent Component Analysis (ICA), which isolates and refines critical attributes for accurate classification. The SONBC classifier then accomplishes exceptional performance metrics, exhibiting recall of 98.45%, precision of 95.79%, accuracy of 97.48%, and an F1 score of 97.7%, demonstrating its capacity to minimize diagnostic errors and certify reliable results. These metrics highlight its ability in reducing false negatives and positives, which are critical for timely and accurate diagnoses. By providing rapid and precise identification of microorganisms, SONBC has the possibility to revolutionize infectious disease diagnostics, empowering medical professionals to make informed treatment decisions quickly. This approach not only enhances patient results by assisting targeted therapies but also reduces diagnostic delays, which are critical in controlling the spread of infectious diseases. For its successful incorporation into clinical workflows, it needs to certify the technology's safety, effectuality, and accessibility. SONBC represents a transformative advancement in the use of AI for infectious disease diagnostics, presenting a scalable solution to global healthcare challenges and paving the way for more dependable and efficient diagnostic methods.

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How to cite

Garg, P., Mane, M., Kumar, A., Panigrahi, R., Tevatia, M. S., & Mahajan, S. (2025). Artificial intelligence based microorganism image analysis for infectious disease diagnostics. Multidisciplinary Science Journal, 7, 2025ss0418. https://doi.org/10.31893/multiscience.2025ss0418
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