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The improvement of automatic scanning microscope based on intelligent systems to identify Mycobacterium tuberculosis
Rulaningtyas R.a, Suksmono A.B.a, Mengko T.L.R.a, Saptawati P.a, Winarnob
a School of Electrical Engineering and Informatics, Bandung Institute of Technology, Indonesia
b Airlangga University, 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]This research yielded the conventional light microscope which could do screening and identification of mycobacterium tuberculosis automatically in sputum smear slide with Ziehl-Neelsen staining. The tool consists of electromechanical side which was assembled to move the X-Y direction of microscope desk automatically. The microscope was provided with the computer aided diagnose software to identify mycobacterium tuberculosis which it consists of image processing, segmentation, feature extraction, and classification methods. The most important in software development in this research is the segmentation process. It could influence the accuracy of mycobacterium tuberculosis observation. We tried some methods on segmentation in which k-Nearest Neighbors gave the better accuracy than other methods. But k-Nearest Neighbors gave the long computational times. After segmentation process, we did classification to the reddish object using neural network with feature extraction based on geometrical shape to become neural network input. The neural network gave very good accuracy 100% on classification of mycobacterium tuberculosis and not mycobacterium tuberculosis. © 2014 IJED.[/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][/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,Feature extraction,Image processing,Microscope,Segmentation[/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][/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]