[vc_empty_space][vc_empty_space]
Automatic diabetic retinopathy classification with efficientnet
Lazuardi R.N.a, Abiwinanda N.a, Suryawan T.H.a, Hanif M.a, Handayani 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]© 2020 IEEE.Using the recent known EfficientNet architecture of deep convolutional neural network (CNN), we present an automatic detection of diabetic retinopathy (DR) from given retinal images. We experiment with subsets of the Kaggle diabetic retinopathy dataset consisting of retinal images with varied diagnostic quality. To address the quality variation, we incorporate two preprocessing steps, i.e. contrast limited adaptive histogram equalization (CLAHE) and image central cropping. We trained EfficientNet-B4 and EfficientNet-B5 model on two Kaggle subsets with different class proportions. In this paper, we propose an automatic early diagnosis of diabetic retinopathy which gained 0.7922 / 83.87% and 0.7931 / 83.89% of quadratic weight kappa and accuracy score on EfficientNet-B4 and EfficientNet-B5 respectively.[/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]Automatic Detection,Contrast Limited Adaptive Histogram Equalization (CLAHE),Diabetic retinopathy,Diagnostic quality,Different class,Early diagnosis,Pre-processing step,Quality variation[/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 Task,Deep Learning,Diabetic Retinopathy,Progressive Resizing[/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/TENCON50793.2020.9293941[/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]