Abstract
This paper presented simple approach that automatically detects Neisseria Bacteria cell in the cerebrospinal fluid smear images. The proposed methodology mainly consists of cerebrospinal fluid smear images acquisition, transformation form red, green, blue smear images in to other color spaces. This step followed by subbing images and segmenting the images to extracting the images features then validation and classifying the Bacteria images based in features extracted using neural networks. The proposed diagnosis for Neisseria Bacteria through neural network techniques has performed high-precision performance in some suggested groups.
Highlights
Meningitis is a health problem of the thin lining that surround the brain and spinal cord[1]
This paper aims to develop image processing and machine learning (ML) techniques to achieve higher accuracy in diagnosing Neisseria bacterial meningitis and optimal classification of meningococci[9]
Color images of cerebrospinal fluid (CSF) smear first obtained as input to the diagnostic process, processing the digital images with treatment programs to obtain clear images representing infection with bacterial parasites or images representing the natural components of the spinal fluid with its natural components without infected by Bacteria
Summary
Meningitis is a health problem of the thin lining that surround the brain and spinal cord[1]. The type caused by bacteria is considered the most dangerous and can cause disabling, including brain injury and hearing loss, meningitis be diagnosed after death, due to the delay in diagnosis by the complex chemical and physical methods used to analyze patient samples. There are several types of meningitis infection[2]. The most important test in determining or excluding meningitis is the analysis of cerebrospinal fluid (CSF) through a lumbar puncture (LP, Spinal tap). CSF samples are checked for the presence of white blood cells, red blood cells, protein content and glucose concentration levels decreased is one of the most important indicators of bacterial meningitis [3]
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