Application of Artificial Neural Network for Diagnosis of Cerebrospinal Meningitis
Abstract
The non-systemization of meningitis diagnosis procedures introduces varying degrees of subjectivity at different stages in the process. This reduces final objectivity of accuracy and increases diagnosis time. To reduce the effects of these shortcomings, an Artificial Intelligence (AI) method for automatic diagnosis of meningitis from gram-stained sputum smear microscopy images, using image processing techniques and Artificial Neural Network (ANN) is presented in this research. An intelligent method of meningitis diagnosis through the application of ANN using image processing techniques was achieved through blood samples collected from the patient and placed in a special dish for microorganism growth observation particularly bacteria. Extraction of image data for cerebrospinal fluid (CSF) sample from the patients were also investigated for meningitis. Segmentation by cascade adaptive threshold-based approach was used to segment meningitis bacilli by pixel intensity value due to gram-staining. A multi-layer (ML) ANN with scaled conjugate gradient descent back propagation training algorithm was used to finally classify the presence or absence of TB bacilli in the pre-processed input image.
MATLAB image processing and Neural Network Toolboxes was used to simulate the procedure. Results of the ANN classifier gave a Mean Square Error (MSE) of 0.025 and accuracy of 94.7%. These results showed that image processing can help to detect the presence or absence of meningitis bacilli in gram-stained CSF smear samples.
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