Betty Purnamasari, Betty
Bachelor of Biomedical Engineering Study Program, Physics Department, Faculty of Science and Technology, Universitas Airlangga

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APPLICATION OF NEURAL NETWORKS ON BLOOD SERUM IMAGE FOR EARLY DETECTION OF TYPHUS Purnamasari, Betty; Arisgraha, Franky; Astuti, Suryani Dyah
Indonesian Journal of Tropical and Infectious Disease Vol 4, No 4 (2013)
Publisher : Institute of Topical Disease

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (970.71 KB)

Abstract

Background: Typhus is a disease caused by Salmonella typhi, Salmonella paratyphi A Salmonella parathypi B, dan Salmonella paratyphi C bacteria that attacks digestive tract and caused infection in small intestine. The common test that performed in the laboratory is widal test. The result reading of the widal test still processed manually with looking the turbidity caused by the agglutination. Aim: The research was made to decrease human error by creating a program based on artificial neural network (ANN) with learning vector quantization (LVQ) method. Method: Input of this program is image of blood serum that has reacted with widal reagen. Image procesing start with grayscaling, filtering, and thresholding. Result: Output of this program is divided into two classes, normal and typhus detected. Conclusion: From this experiment result that using 24 testing data, gives the accuracy of this program 95.833% with 1 error result from 24 testing data.
APPLICATION OF NEURAL NETWORKS ON BLOOD SERUM IMAGE FOR EARLY DETECTION OF TYPHUS Purnamasari, Betty; Arisgraha, Franky; Astuti, Suryani Dyah
Indonesian Journal of Tropical and Infectious Disease Vol 4, No 4 (2013)
Publisher : Institute of Topical Disease

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (970.71 KB) | DOI: 10.20473/ijtid.v4i4.234

Abstract

Background: Typhus is a disease caused by Salmonella typhi, Salmonella paratyphi A Salmonella parathypi B, dan Salmonella paratyphi C bacteria that attacks digestive tract and caused infection in small intestine. The common test that performed in the laboratory is widal test. The result reading of the widal test still processed manually with looking the turbidity caused by the agglutination. Aim: The research was made to decrease human error by creating a program based on artificial neural network (ANN) with learning vector quantization (LVQ) method. Method: Input of this program is image of blood serum that has reacted with widal reagen. Image procesing start with grayscaling, filtering, and thresholding. Result: Output of this program is divided into two classes, normal and typhus detected. Conclusion: From this experiment result that using 24 testing data, gives the accuracy of this program 95.833% with 1 error result from 24 testing data.