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Automatic detection system of cervical cancer cells using color intensity classification
Supriyanto E.a, Pista N.A.M.a, Ismail L.H.a, Rosidi B.a, Mengko T.L.b
a Biotechnology Research Alliance, Faculty of Biomedical Engineering and Health Science, Universiti Teknologi Malaysia UTM, Malaysia
b Biomedical Engineering Division, 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]The conventional Pap smear has been undeniably responsible in reducing the number of incidence and mortality of cervical cancer. However, few concerns have arisen such as the shortage of skilled and experienced pathologists and the increasing workload as a result of more individuals having gained access to preventive health care which eventually will make the reviewing procedure becomes time consuming and highly prone to human errors. In order to solve this problem, an automated detection system of cervical cancer cells has been developed. The detection of cervical cancer cells is based on the morphology of the cells and level set operations. Test result shows, that by using color intensity classification the system is able to differentiate between normal and cancerous cells. This system will hopefully help the pathologist to reduce the work-load and minimize human error while maintaining and improving the accuracy of the system.[/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]Automated detection,Automatic Detection,Cancerous cells,Cervical cancer cells,Cervical cancers,Color intensity,Color intensity distribution,Human errors,Level Set,Pap smear[/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]Automated detection,Cervical cancer,Color intensity distribution,Morphology,Pap smear[/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]