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Performing high accuracy of the system for cataract detection using statistical texture analysis and K-Nearest Neighbor
Fuadah Y.N.a, Setiawan A.W.a, Mengko T.L.R.a
a Electrical Engineering Department, Institut Teknologi Bandung, Biomedical Engineering Reasearch Group, 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]© 2015 IEEE.Early detection of cataract considered as an important solution to prevent the increasing number of cataract in developing country, especially in Indonesia. A cataract will be a serious public health problem as a leading cause of blindness if there is a delay in handling it. In this paper, we discuss about the performing high accuracy of the system for cataract detection using statistical texture analysis and K-Nearest Neighbor (K-NN). In training steps, the feature extraction method uses Gray Level Co-occurrence Matrix (GLCM) to get the texture feature value of contrast, dissimilarity and uniformity that appearance in the pupil area of the training images. In testing steps, the testing images will be classified using K-NN method to normal or cataract condition. Based on the result of 10 times experiments for 160 eyes images that consist of 40 normal images and 40 cataract images as the training data and 40 normal images and 40 cataract images as the testing data, the statistical texture analysis and K-NN perform high accuracy for detecting cataract with average accuracy 94.5%.[/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]cataract,Feature extraction methods,Gray level co occurrence matrix(GLCM),K-nearest neighbors,Texture analysis,Texture features,Training data,Training image[/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]cataract,k-nn,statistical texture analysis[/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/ISITIA.2015.7219958[/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]