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Automatic classification of tuberculosis bacteria using neural network
Rulaningtyas R.a, Suksmono A.B.a, Mengko T.L.R.a
a School of Electrical Engineering and Informatics, Bandung Institute of Technology, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]Sputum smear microscopy analysis is the important thing for early diagnosis tuberculosis diseases. A lot of patients in tuberculosis medical center cause the doctors and the technicians have heavy duty. Our research result can be used to reduce technician involvement in screening for tuberculosis and would be useful in laboratories. This research is early step to find appropriate method for identifying tuberculosis bacteria. The analysis of sputum smear requires highly trained to avoid high errors. It needs an appropriate pattern recognition and classification of tuberculosis bacteria. Before classification, geometric features of tuberculosis cell image are found from its binary image. The geometric features of tuberculosis cell image consist of circularity, compactness, eccentricity, and tortuosity. These geometric features would become inputs to the neural network trained with backpropagation method. The 100 samples would be divided into 75 training samples and 25 testing samples. After getting optimum weights and architectures of neural network with 20 neurons hidden layer, 0.05 learning rate, and 0.9 momentum, this network is used for classifying tuberculosis bacteria into two categories: tuberculosis bacteria or not. Results presented for several image taken from different binary cell image show that neural network classifies the presence of tuberculosis bacteria image accurately with mean square error 0.000368, error classification zero in training and testing processes for the data that is used in this research. © 2011 IEEE.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Automatic classification,Cell images,Early diagnosis,Error classification,Geometric feature,Heavy duty,Hidden layers,Learning rates,Medical center,Pattern recognition and classification,Research results,Testing samples,Training and testing,Training sample,Tuberculosis bacteria[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]classification,geometric features,microscopy,neural network,Tuberculosis bacteria[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICEEI.2011.6021502[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]