Diagnosing Bacteria Samples Using Data Mining: Review study

• Main Authors: Badr, Ahmed Adnan, Abbas, Thekra, AboKsour, Mohammed Fadhel
• Format: Conference Proceeding
• Language: English
• Subjects: Artificial Intelligence (AI); Bacteria classification; Buildings; Convolution Neural Network; Data Mining; Deep learning; Image color analysis; Microorganisms; Microscopy; Shape; Support Vector Machine; Support vector machine classification
• ISSN: 2831-753X
• DOI: 10.1109/IICETA54559.2022.9888705

Bacteria are implicated in a lot of biological and chemical activities, some of which are dangerous and others beneficial. Bacterial samples go through several stages before identification. Some of these stages are done visually with a microscope to detect the bacteria's shape and color of the gram stain, while others include exposing these samples to chemical and organic substances. Researchers have developed intelligence computer systems capable of diagnosing and classifying bacteria in order to minimize the amount of human labor and increase diagnosis accuracy. This paper will provide a detailed look at previous studies that tried to find solutions to the problem of diagnosing and classifying bacteria samples using artificial intelligence techniques such as deep learning, machine learning and data mining, as well as analyzing the results of these studies and clarifying the challenges of building comprehensive systems capable of performing this task.