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A Semiautomatic Sperm Cell Data Annotator for Convolutional Neural Network
Hidayatullah P.a, Mengko T.E.R.a, Munir R.a, Barlian A.a
a Institut Teknologi Bandung, School of Electrical Engineering and Informatics, Bandung, 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]© 2019 IEEE.Convolutional Neural Network (CNN) is one of deep learning methods which is very powerful for solving image processing and computer vision problems. The powerfulness of this method relies on the number and the quality of the training data. The need for numerous and valid annotated training data is crucial. Manual annotation is still the most popular option for obtaining a valid data annotation. There are some manual annotation systems available. Nevertheless, manually annotating a massive amount of data is remarkably tiresome and need plenty of time. This is the problem that we addressed. In this paper, we propose a system to annotate a CNN training data semi-automatically to accelerate the annotation process. We use an extreme case in which the number of objects to annotate inside a video frame is plentiful (334 – 472 objects). We compare the accuracy of its annotation and the speed of the annotation process to the available annotation systems. The result is that our proposed method reduces the average annotation time up to 48.7%.[/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]Annotated training data,Annotation systems,Data annotation,Data preparation,Image processing and computer vision,Learning methods,Manual annotation,Training data[/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]Convolutional Neural Network,data annotation system,data preparation,deep learning,sperm detection[/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]This work is supported by Lembaga Pengelola Dana Pendidikan RI[/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/ICSITech46713.2019.8987471[/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]