2021
DOI: 10.1364/boe.420935
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Abstract: The accurate diagnosis of various esophageal diseases at different stages is crucial for providing precision therapy planning and improving 5-year survival rate of esophageal cancer patients. Automatic classification of various esophageal diseases in gastroscopic images can assist doctors to improve the diagnosis efficiency and accuracy. The existing deep learning-based classification method can only classify very few categories of esophageal diseases at the same time. Hence, we proposed a novel efficient chan… Show more

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Cited by 28 publications
(16 citation statements)
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“…Images were resized to 224 × 224 pixels for the experiments. As shown in Figure 1, the raw images that contained unnecessary background and text were removed during pre-processing, and the class balancing technique was applied with ECA-DDCNN [15]. Eighty percent of the data were used for training and 20% for testing.…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…Images were resized to 224 × 224 pixels for the experiments. As shown in Figure 1, the raw images that contained unnecessary background and text were removed during pre-processing, and the class balancing technique was applied with ECA-DDCNN [15]. Eighty percent of the data were used for training and 20% for testing.…”
Section: Methodsmentioning
confidence: 99%
“…To improve the suggestive power of a CNN network, several recent studies have shown the benefit of enhancing the spatial encoding ability of CNN via spatial attention modules. Other studies have indicated promising CNN-based approaches for disease classifications [9,12,[15][16][17][18][19][20]. In addition, spatial attention mechanisms have been studied and applied to related tasks [16,17,[21][22][23].…”
Section: Introductionmentioning
confidence: 99%
“…The precision and accuracy of classification of 20 amino acids on Average20 and Average10 datasets. From a to h are: a. ECA network with absorption rate as input; b. ECA network with refractive index as input; c. plain CNN with hybrid spectrum as input; d. ECA-DDCNN [33] with hybrid spectrum as input; e. ECA-Resnet50 [24] with hybrid spectrum as input; f. ECA-Resnet101 [24] with hybrid spectrum as input; g. CNN-BiGRU referred from [23]; h. CNN referred from [22]. Ours denotes the ECA network with hybrid spectrum as input.…”
Section: The Effects Of Hybrid Spectrum and Eca Modulementioning
confidence: 99%
“…Liu et al [ 24 ] brought forward a transfer learning framework by fine-tuning pretrained models, such as VGGNets, Inception, and ResNets, to successfully classify gastric images into chronic gastritis, low-grade neoplasia, and early gastric cancer. Du et al [ 25 ] proposed an efficient channel attention deep dense convolutional neural network that can classify diseases into four categories with a higher area under the curve value. We can see that the above-mentioned deep learning models could achieve obvious success in esophagus classification.…”
Section: Related Workmentioning
confidence: 99%