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Effect of Image Downsizing and Color Reduction on Skin Cancer Pre-screening

Setiawan A.W.a, Faisal A.b, Resfita N.b

a School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, Indonesia
b Biomedical Engineering, Institut Teknologi Sumatera, South Lampung, 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]© 2020 IEEE.Every year, the skin cancer burden is increasing due to ultraviolet exposure caused by the gradual thinning of Earth’s ozone layer. One of the main methods to detect skin lesion is using image processing techniques, including the use of machine learning. This research tries to explore the effect of skin cancer image downsizing and color dimensional reduction using k-means clustering on skin lesion pre-screening. The contribution of this research is that for skin cancer pre-screening using CNN, the optimal combination that can be used in terms of accuracy (training and validation); image size; the number of colors; and computing time, is the combination of 8 colors and 200× 150 pixels image. The significance of this study is that the penetration of smartphones as low-cost computation resources are tremendous, particularly in Indonesia. This device can be used to detect the skin lesion in an easy way due to it is already equipped with a camera, processor, memory, and other computing peripherals.[/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]Color reduction,Computation resources,Computing time,Dimensional reduction,Image downsizing,Image processing technique,Optimal combination,Ultraviolet exposure[/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]image downsizing,k-means,machine-learning,pre-screening,skin cancer[/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/ISITIA49792.2020.9163734[/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]